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0 | Deterministic Image-to-Image Translation via Denoising Brownian Bridge Models with Dual Approximators | [
"Bohan Xiao",
"Peiyong Wang",
"Qisheng He",
"Ming Dong"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Xiao_Deterministic_Image-to-Image_Translation_via_Denoising_Brownian_Bridge_Models_with_Dual_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Xiao_Deterministic_Image-to-Image_Translation_via_Denoising_Brownian_Bridge_Models_with_Dual_CVPR_2025_paper.pdf | null | null | @InProceedings{Xiao_2025_CVPR,
author = {Xiao, Bohan and Wang, Peiyong and He, Qisheng and Dong, Ming},
title = {Deterministic Image-to-Image Translation via Denoising Brownian Bridge Models with Dual Approximators},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {28232-28241}
} | Image-to-Image (I2I) translation involves converting an im- age from one domain to another. Deterministic I2I transla- tion, such as in image super-resolution, extends this con- cept by guaranteeing that each input generates a consistent and predictable output, closely matching the ground truth (GT) with high fidelity. In this paper, we propose a denois- ing Brownian bridge model with dual approximators (Dual- approx Bridge), a novel generative model that exploits the Brownian bridge dynamics and two neural network-based approximators (one for forward and one for reverse pro- cess) to produce faithful output with negligible variance and high image quality in I2I translations. Our extensive exper- iments on benchmark datasets including image generation and super-resolution demonstrate the consistent and supe- rior performance of Dual-approx Bridge in terms of im- age quality and faithfulness to GT when compared to both stochastic and deterministic baselines. Project page and code: https://github.com/bohan95/dual-app-bridge | [
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1 | Towards Source-Free Machine Unlearning | [
"Sk Miraj Ahmed",
"Umit Yigit Basaran",
"Dripta S. Raychaudhuri",
"Arindam Dutta",
"Rohit Kundu",
"Fahim Faisal Niloy",
"Basak Guler",
"Amit K. Roy-Chowdhury"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Ahmed_Towards_Source-Free_Machine_Unlearning_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Ahmed_Towards_Source-Free_Machine_Unlearning_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ahmed_Towards_Source-Free_Machine_CVPR_2025_supplemental.pdf | null | @InProceedings{Ahmed_2025_CVPR,
author = {Ahmed, Sk Miraj and Basaran, Umit Yigit and Raychaudhuri, Dripta S. and Dutta, Arindam and Kundu, Rohit and Niloy, Fahim Faisal and Guler, Basak and Roy-Chowdhury, Amit K.},
title = {Towards Source-Free Machine Unlearning},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {4948-4957}
} | As machine learning become more pervasive and data privacy regulations evolve, the ability to remove private or copyrighted information from trained models is becoming an increasingly critical requirement. Existing unlearning methods often rely on the assumption of having access to the entire training dataset during the forgetting process. However, this assumption may not hold true in practical scenarios where the original training data may not be accessible, i.e., the source-free setting. To address this challenge, we focus on the source-free unlearning scenario, where an unlearning algorithm must be capable of removing specific data from a trained model without requiring access to the original training dataset. Building on recent work, we present a method that can estimate the Hessian of the unknown remaining training data, a crucial component required for efficient unlearning. Leveraging this estimation technique, our method enables efficient zero-shot unlearning while providing robust theoretical guarantees on the unlearning performance, while maintaining performance on the remaining data. Extensive experiments over a wide range of datasets verify the efficacy of our method. | [
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] |
2 | Uni4D: Unifying Visual Foundation Models for 4D Modeling from a Single Video | [
"David Yifan Yao",
"Albert J. Zhai",
"Shenlong Wang"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Yao_Uni4D_Unifying_Visual_Foundation_Models_for_4D_Modeling_from_a_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Yao_Uni4D_Unifying_Visual_Foundation_Models_for_4D_Modeling_from_a_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yao_Uni4D_Unifying_Visual_CVPR_2025_supplemental.zip | http://arxiv.org/abs/2503.21761 | @InProceedings{Yao_2025_CVPR,
author = {Yao, David Yifan and Zhai, Albert J. and Wang, Shenlong},
title = {Uni4D: Unifying Visual Foundation Models for 4D Modeling from a Single Video},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {1116-1126}
} | This paper presents a unified approach to understanding dynamic scenes from casual videos. Large pretrained vision foundation models, such as vision-language, video depth prediction, motion tracking, and segmentation models, offer promising capabilities. However, training a single model for comprehensive 4D understanding remains challenging. We introduce Uni4D, a multi-stage optimization framework that harnesses multiple pretrained models to advance dynamic 3D modeling, including static/dynamic reconstruction, camera pose estimation, and dense 3D motion tracking. Our results show state-of-the-art performance in dynamic 4D modeling with superior visual quality. Notably, Uni4D requires no retraining or fine-tuning, highlighting the effectiveness of repurposing visual foundation models for 4D understanding. Code and more results are available at: https://davidyao99.github.io/uni4d. | [
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] |
3 | DynScene: Scalable Generation of Dynamic Robotic Manipulation Scenes for Embodied AI | [
"Sangmin Lee",
"Sungyong Park",
"Heewon Kim"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Lee_DynScene_Scalable_Generation_of_Dynamic_Robotic_Manipulation_Scenes_for_Embodied_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_DynScene_Scalable_Generation_of_Dynamic_Robotic_Manipulation_Scenes_for_Embodied_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lee_DynScene_Scalable_Generation_CVPR_2025_supplemental.pdf | null | @InProceedings{Lee_2025_CVPR,
author = {Lee, Sangmin and Park, Sungyong and Kim, Heewon},
title = {DynScene: Scalable Generation of Dynamic Robotic Manipulation Scenes for Embodied AI},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {12166-12175}
} | Robotic manipulation in embodied AI critically depends on large-scale, high-quality datasets that reflect realistic object interactions and physical dynamics. However, existing data collection pipelines are often slow, expensive, and heavily reliant on manual efforts. We present DynScene, a diffusion-based framework for generating dynamic robotic manipulation scenes directly from textual instructions. Unlike prior methods that focus solely on static environments or isolated robot actions, DynScene decomposes the generation into two phases static scene synthesis and action trajectory generation allowing fine-grained control and diversity. Our model enhances realism and physical feasibility through scene refinement (layout sampling, quaternion quantization) and leverages residual action representation to enable action augmentation, generating multiple diverse trajectories from a single static configuration. Experiments show DynScene achieves 26.8x faster generation, 1.84x higher accuracy, and 28% greater action diversity than human-crafted data. Furthermore, agents trained with DynScene exhibit up to 19.4% higher success rates across complex manipulation tasks. Our approach paves the way for scalable, automated dataset generation in robot learning. | [
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4 | DiffLocks: Generating 3D Hair from a Single Image using Diffusion Models | [
"Radu Alexandru Rosu",
"Keyu Wu",
"Yao Feng",
"Youyi Zheng",
"Michael J. Black"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Rosu_DiffLocks_Generating_3D_Hair_from_a_Single_Image_using_Diffusion_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Rosu_DiffLocks_Generating_3D_Hair_from_a_Single_Image_using_Diffusion_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Rosu_DiffLocks_Generating_3D_CVPR_2025_supplemental.zip | http://arxiv.org/abs/2505.06166 | @InProceedings{Rosu_2025_CVPR,
author = {Rosu, Radu Alexandru and Wu, Keyu and Feng, Yao and Zheng, Youyi and Black, Michael J.},
title = {DiffLocks: Generating 3D Hair from a Single Image using Diffusion Models},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {10847-10857}
} | We address the task of generating 3D hair geometry from a single image, which is challenging due to the diversity of hairstyles and the lack of paired image-to-3D hair data. Previous methods are primarily trained on synthetic data and cope with the limited amount of such data by using low-dimensional intermediate representations, such as guide strands and scalp-level embeddings, that require post-processing to decode, upsample, and add realism. These approaches fail to reconstruct detailed hair, struggle with curly hair, or are limited to handling only a few hairstyles. To overcome these limitations, we propose DiffLocks, a novel framework that enables detailed reconstruction of a wide variety of hairstyles directly from a single image. First, we address the lack of 3D hair data by automating the creation of the largest synthetic hair dataset to date, containing 40K hairstyles. Second, we leverage the synthetic hair dataset to learn an image-conditioned diffusion-transfomer model that generates accurate 3D strands from a single frontal image. By using a pretrained image backbone, our method generalizes to in-the-wild images despite being trained only on synthetic data. Our diffusion model predicts a scalp texture map in which any point in the map contains the latent code for an individual hair strand. These codes are directly decoded to 3D strands without post-processing techniques. Representing individual strands, instead of guide strands, enables the transformer to model the detailed spatial structure of complex hairstyles. With this, DiffLocks can recover highly curled hair, like afro hairstyles, from a single image for the first time. Data and code is available at https://radualexandru.github.io/difflocks | [
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] |
5 | Hyperbolic Category Discovery | [
"Yuanpei Liu",
"Zhenqi He",
"Kai Han"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Hyperbolic_Category_Discovery_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Hyperbolic_Category_Discovery_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Hyperbolic_Category_Discovery_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2504.06120 | @InProceedings{Liu_2025_CVPR,
author = {Liu, Yuanpei and He, Zhenqi and Han, Kai},
title = {Hyperbolic Category Discovery},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {9891-9900}
} | Generalized Category Discovery (GCD) is an intriguing open-world problem that has garnered increasing attention. Given a dataset that includes both labelled and unlabelled images, GCD aims to categorize all images in the unlabelled subset, regardless of whether they belong to known or unknown classes. In GCD, the common practice typically involves applying a spherical projection operator at the end of the self-supervised pretrained backbone, operating within Euclidean or spherical space. However, both of these spaces have been shown to be suboptimal for encoding samples that possess hierarchical structures. In contrast, hyperbolic space exhibits exponential volume growth relative to radius, making it inherently strong at capturing the hierarchical structure of samples from both seen and unseen categories. Therefore, we propose to tackle the category discovery challenge in the hyperbolic space. We introduce HypCD, a simple Hyperbolic framework for learning hierarchy-aware representations and classifiers for generalized Category Discovery. HypCD first transforms the Euclidean embedding space of the backbone network into hyperbolic space, facilitating subsequent representation and classification learning by considering both hyperbolic distance and the angle between samples. This approach is particularly helpful for knowledge transfer from known to unknown categories in GCD. We thoroughly evaluate HypCD on public GCD benchmarks, by applying it to various baseline and state-of-the-art methods, consistently achieving significant improvements. | [
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6 | The Language of Motion: Unifying Verbal and Non-verbal Language of 3D Human Motion | [
"Changan Chen",
"Juze Zhang",
"Shrinidhi K. Lakshmikanth",
"Yusu Fang",
"Ruizhi Shao",
"Gordon Wetzstein",
"Li Fei-Fei",
"Ehsan Adeli"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Chen_The_Language_of_Motion_Unifying_Verbal_and_Non-verbal_Language_of_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_The_Language_of_Motion_Unifying_Verbal_and_Non-verbal_Language_of_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_The_Language_of_CVPR_2025_supplemental.zip | http://arxiv.org/abs/2412.10523 | @InProceedings{Chen_2025_CVPR,
author = {Chen, Changan and Zhang, Juze and Lakshmikanth, Shrinidhi K. and Fang, Yusu and Shao, Ruizhi and Wetzstein, Gordon and Fei-Fei, Li and Adeli, Ehsan},
title = {The Language of Motion: Unifying Verbal and Non-verbal Language of 3D Human Motion},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {6200-6211}
} | Human communication is inherently multimodal, involving a combination of verbal and non-verbal cues such as speech, facial expressions, and body gestures. Modeling these behaviors is essential for understanding human interaction and for creating virtual characters that can communicate naturally in applications like games, films, and virtual reality. However, existing motion generation models are typically limited to specific input modalities--either speech, text, or motion data--and cannot fully leverage the diversity of available data. In this paper, we propose a novel framework that unifies verbal and non-verbal language using multimodal language models for human motion understanding and generation. This model is flexible in taking text, speech, and motion or any combination of them as input. Coupled with our novel pre-training strategy, our model not only achieves state-of-the-art performance on co-speech gesture generation but also requires much less data for training. Our model also unlocks an array of novel tasks such as editable gesture generation and emotion prediction from motion. We believe unifying the verbal and non-verbal language of human motion is essential for real-world applications, and language models offer a powerful approach to achieving this goal. | [
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7 | CALICO: Part-Focused Semantic Co-Segmentation with Large Vision-Language Models | [
"Kiet A. Nguyen",
"Adheesh Juvekar",
"Tianjiao Yu",
"Muntasir Wahed",
"Ismini Lourentzou"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Nguyen_CALICO_Part-Focused_Semantic_Co-Segmentation_with_Large_Vision-Language_Models_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Nguyen_CALICO_Part-Focused_Semantic_Co-Segmentation_with_Large_Vision-Language_Models_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Nguyen_CALICO_Part-Focused_Semantic_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2412.19331 | @InProceedings{Nguyen_2025_CVPR,
author = {Nguyen, Kiet A. and Juvekar, Adheesh and Yu, Tianjiao and Wahed, Muntasir and Lourentzou, Ismini},
title = {CALICO: Part-Focused Semantic Co-Segmentation with Large Vision-Language Models},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {4550-4561}
} | Recent advances in Large Vision-Language Models (LVLMs) have enabled general-purpose vision tasks through visual instruction tuning. While existing LVLMs can generate segmentation masks from text prompts for single images, they struggle with segmentation-grounded reasoning across images, especially at finer granularities such as object parts. In this paper, we introduce the new task of part-focused semantic co-segmentation, which involves identifying and segmenting common objects and their constituent common and unique parts across images. To address this task, we present CALICO, the first LVLM designed for multi-image part-level reasoning segmentation. CALICO features two key components, a novel Correspondence Extraction Module that identifies semantic part-level correspondences, and Correspondence Adaptation Modules that embed this information into the LVLM to facilitate multi-image understanding in a parameter-efficient manner. To support training and evaluation, we curate MixedParts, a large-scale multi-image segmentation dataset containing 2.4M samples across 44K images spanning diverse object and part categories. Experimental results demonstrate that CALICO, with just 0.3% of its parameters finetuned, achieves strong performance on this challenging task. | [
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] |
8 | Task Preference Optimization: Improving Multimodal Large Language Models with Vision Task Alignment | [
"Ziang Yan",
"Zhilin Li",
"Yinan He",
"Chenting Wang",
"Kunchang Li",
"Xinhao Li",
"Xiangyu Zeng",
"Zilei Wang",
"Yali Wang",
"Yu Qiao",
"Limin Wang",
"Yi Wang"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Yan_Task_Preference_Optimization_Improving_Multimodal_Large_Language_Models_with_Vision_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_Task_Preference_Optimization_Improving_Multimodal_Large_Language_Models_with_Vision_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yan_Task_Preference_Optimization_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2412.19326 | @InProceedings{Yan_2025_CVPR,
author = {Yan, Ziang and Li, Zhilin and He, Yinan and Wang, Chenting and Li, Kunchang and Li, Xinhao and Zeng, Xiangyu and Wang, Zilei and Wang, Yali and Qiao, Yu and Wang, Limin and Wang, Yi},
title = {Task Preference Optimization: Improving Multimodal Large Language Models with Vision Task Alignment},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {29880-29892}
} | Current multimodal large language models (MLLMs) struggle with fine-grained or precise understanding of visuals although they give comprehensive perception and reasoning in a spectrum of vision applications. Recent studies either develop tool-using or unify specific visual tasks into the autoregressive framework, often at the expense of overall multimodal performance. To address this issue and enhance MLLMs with visual tasks in a scalable fashion, we propose Task Preference Optimization (TPO), a novel method that utilizes differentiable task preferences derived from typical fine-grained visual tasks. TPO introduces learnable task tokens that establish connections between multiple task-specific heads and the MLLM. By leveraging rich visual labels during training, TPO significantly enhances the MLLM's multimodal capabilities and task-specific performance. Through multi-task co-training within TPO, we observe synergistic benefits that elevate individual task performance beyond what is achievable through single-task training methodologies. Our instantiation of this approach with VideoChat and LLaVA demonstrates an overall 14.6% improvement in multimodal performance compared to baseline models. Additionally, MLLM-TPO demonstrates robust zero-shot capabilities across various tasks, performing comparably to state-of-the-art supervised models. | [
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9 | Cross-modal Causal Relation Alignment for Video Question Grounding | [
"Weixing Chen",
"Yang Liu",
"Binglin Chen",
"Jiandong Su",
"Yongsen Zheng",
"Liang Lin"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Cross-modal_Causal_Relation_Alignment_for_Video_Question_Grounding_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Cross-modal_Causal_Relation_Alignment_for_Video_Question_Grounding_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Cross-modal_Causal_Relation_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.07635 | @InProceedings{Chen_2025_CVPR,
author = {Chen, Weixing and Liu, Yang and Chen, Binglin and Su, Jiandong and Zheng, Yongsen and Lin, Liang},
title = {Cross-modal Causal Relation Alignment for Video Question Grounding},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {24087-24096}
} | Video question grounding (VideoQG) requires models to answer the questions and simultaneously infer the relevant video segments to support the answers. However, existing VideoQG methods usually suffer from spurious cross-modal correlations, leading to a failure to identify the dominant visual scenes that align with the intended question. Moreover, vision-language models exhibit unfaithful generalization performance and lack robustness on challenging downstream tasks such as VideoQG. In this work, we propose a novel VideoQG framework named Cross-modal Causal Relation Alignment (CRA), to eliminate spurious correlations and improve the causal consistency between question-answering and video temporal grounding. Our CRA involves three essential components: i) Gaussian Smoothing Grounding (GSG) module for estimating the time interval via cross-modal attention, which is de-noised by an adaptive Gaussian filter, ii) Cross-Modal Alignment (CMA) enhances the performance of weakly supervised VideoQG by leveraging bidirectional contrastive learning between estimated video segments and QA features, iii) Explicit Causal Intervention (ECI) module for multimodal deconfounding, which involves front-door intervention for vision and back-door intervention for language. Extensive experiments on two VideoQG datasets demonstrate the superiority of our CRA in discovering visually grounded content and achieving robust question reasoning. Codes are available at https://github.com/WissingChen/CRA-GQA. | [
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10 | Words or Vision: Do Vision-Language Models Have Blind Faith in Text? | [
"Ailin Deng",
"Tri Cao",
"Zhirui Chen",
"Bryan Hooi"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Deng_Words_or_Vision_Do_Vision-Language_Models_Have_Blind_Faith_in_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Deng_Words_or_Vision_Do_Vision-Language_Models_Have_Blind_Faith_in_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Deng_Words_or_Vision_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.02199 | @InProceedings{Deng_2025_CVPR,
author = {Deng, Ailin and Cao, Tri and Chen, Zhirui and Hooi, Bryan},
title = {Words or Vision: Do Vision-Language Models Have Blind Faith in Text?},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {3867-3876}
} | Vision-Language Models (VLMs) excel in integrating visual and textual information for vision-centric tasks, but their handling of inconsistencies between modalities is underexplored. We investigate VLMs' modality preferences when faced with visual data and varied textual inputs in vision-centered settings.By introducing textual variations to four vision-centric tasks and evaluating ten Vision-Language Models (VLMs), we discover a "blind faith in text" phenomenon: VLMs disproportionately trust textual data over visual data when inconsistencies arise, leading to significant performance drops under corrupted text and raising safety concerns.We analyze factors influencing this text bias, including instruction prompts, language model size, text relevance, token order, and the interplay between visual and textual certainty. While certain factors, such as scaling up the language model size, slightly mitigate text bias, others like token order can exacerbate it due to positional biases inherited from language models. To address this issue, we explore supervised fine-tuning with text augmentation and demonstrate its effectiveness in reducing text bias. Additionally, we provide a theoretical analysis suggesting that the blind faith in text phenomenon may stem from an imbalance of pure text and multi-modal data during training.Our findings highlight the need for balanced training and careful consideration of modality interactions in VLMs to enhance their robustness and reliability in handling multi-modal data inconsistencies. | [
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11 | Diffusion Renderer: Neural Inverse and Forward Rendering with Video Diffusion Models | [
"Ruofan Liang",
"Zan Gojcic",
"Huan Ling",
"Jacob Munkberg",
"Jon Hasselgren",
"Chih-Hao Lin",
"Jun Gao",
"Alexander Keller",
"Nandita Vijaykumar",
"Sanja Fidler",
"Zian Wang"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Liang_Diffusion_Renderer_Neural_Inverse_and_Forward_Rendering_with_Video_Diffusion_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Liang_Diffusion_Renderer_Neural_Inverse_and_Forward_Rendering_with_Video_Diffusion_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liang_Diffusion_Renderer_Neural_CVPR_2025_supplemental.zip | null | @InProceedings{Liang_2025_CVPR,
author = {Liang, Ruofan and Gojcic, Zan and Ling, Huan and Munkberg, Jacob and Hasselgren, Jon and Lin, Chih-Hao and Gao, Jun and Keller, Alexander and Vijaykumar, Nandita and Fidler, Sanja and Wang, Zian},
title = {Diffusion Renderer: Neural Inverse and Forward Rendering with Video Diffusion Models},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {26069-26080}
} | Understanding and modeling lighting effects are fundamental tasks in computer vision and graphics. Classic physically-based rendering (PBR) accurately simulates the light transport, but relies on precise scene representations--explicit 3D geometry, high-quality material properties, and lighting conditions--that are often impractical to obtain in real-world scenarios. Therefore, we introduce Diffusion Renderer, a neural approach that addresses the dual problem of inverse and forward rendering within a holistic framework. Leveraging powerful video diffusion model priors, the inverse rendering model accurately estimates G-buffers from real-world videos, providing an interface for image editing tasks, and training data for the rendering model. Conversely, our rendering model generates photorealistic images from G-buffers without explicit light transport simulation. Specifically, we first train a video diffusion model for inverse rendering on synthetic data, which generalizes well to real-world videos and allows us to auto-label diverse real-world videos. We then co-train our rendering model using both synthetic and auto-labeled real-world data. Experiments demonstrate that Diffusion Renderer effectively approximates inverse and forwards rendering, consistently outperforming the state-of-the-art. Our model enables practical applications from a single video input--including relighting, material editing, and realistic object insertion. | [
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] |
12 | Harnessing Frequency Spectrum Insights for Image Copyright Protection Against Diffusion Models | [
"Zhenguang Liu",
"Chao Shuai",
"Shaojing Fan",
"Ziping Dong",
"Jinwu Hu",
"Zhongjie Ba",
"Kui Ren"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Harnessing_Frequency_Spectrum_Insights_for_Image_Copyright_Protection_Against_Diffusion_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Harnessing_Frequency_Spectrum_Insights_for_Image_Copyright_Protection_Against_Diffusion_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Harnessing_Frequency_Spectrum_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.11071 | @InProceedings{Liu_2025_CVPR,
author = {Liu, Zhenguang and Shuai, Chao and Fan, Shaojing and Dong, Ziping and Hu, Jinwu and Ba, Zhongjie and Ren, Kui},
title = {Harnessing Frequency Spectrum Insights for Image Copyright Protection Against Diffusion Models},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {18653-18662}
} | Diffusion models have achieved remarkable success in novel view synthesis, but their reliance on large, diverse, and often untraceable Web datasets has raised pressing concerns about image copyright protection. Current methods fall short in reliably identifying unauthorized image use, as they struggle to generalize across varied generation tasks and fail when the training dataset includes images from multiple sources with few identifiable (watermarked or poisoned) samples. In this paper, we present novel evidence that diffusion-generated images faithfully preserve the statistical properties of their training data, particularly reflected in their spectral features. Leveraging this insight, we introduce CoprGuard, a robust frequency domain watermarking framework to safeguard against unauthorized image usage in diffusion model training and fine-tuning. CoprGuard demonstrates remarkable effectiveness against a wide range of models, from naive diffusion models to sophisticated text-to-image models, and is robust even when watermarked images comprise a mere 1% of the training dataset. This robust and versatile approach empowers content owners to protect their intellectual property in the era of AI-driven image generation. | [
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] |
13 | Learning to Detect Objects from Multi-Agent LiDAR Scans without Manual Labels | [
"Qiming Xia",
"Wenkai Lin",
"Haoen Xiang",
"Xun Huang",
"Siheng Chen",
"Zhen Dong",
"Cheng Wang",
"Chenglu Wen"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Xia_Learning_to_Detect_Objects_from__Multi-Agent_LiDAR_Scans_without_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Xia_Learning_to_Detect_Objects_from__Multi-Agent_LiDAR_Scans_without_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xia_Learning_to_Detect_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.08421 | @InProceedings{Xia_2025_CVPR,
author = {Xia, Qiming and Lin, Wenkai and Xiang, Haoen and Huang, Xun and Chen, Siheng and Dong, Zhen and Wang, Cheng and Wen, Chenglu},
title = {Learning to Detect Objects from Multi-Agent LiDAR Scans without Manual Labels},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {1418-1428}
} | Unsupervised 3D object detection serves as an important solution for offline 3D object annotation. However, due to the data sparsity and limited views, the clustering-based label fitting in unsupervised object detection often generates low-quality pseudo-labels. Multi-agent collaborative dataset, which involves the sharing of complementary observations among agents, holds the potential to break through this bottleneck. In this paper, we introduce a novel unsupervised method that learns to Detect Objects from Multi-Agent LiDAR scans, termed DOtA, without using labels from external. DOtA first uses the internally shared ego-pose and ego-shape of collaborative agents to initialize the detector, leveraging the generalization performance of neural networks to infer preliminary labels. Subsequently, DOtA uses the complementary observations between agents to perform multi-scale encoding on preliminary labels, then decodes high-quality and low-quality labels. These labels are further used as prompts to guide a correct feature learning process, thereby enhancing the performance of the unsupervised object detection task. Extensive experiments on the V2V4Real and OPV2V datasets show that our DOtA outperforms state-of-the-art unsupervised 3D object detection methods. Additionally, we also validate the effectiveness of the DOtA labels under various collaborative perception frameworks. The code is available at https://github.com/xmuqimingxia/DOtA. | [
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14 | DeepLA-Net: Very Deep Local Aggregation Networks for Point Cloud Analysis | [
"Ziyin Zeng",
"Mingyue Dong",
"Jian Zhou",
"Huan Qiu",
"Zhen Dong",
"Man Luo",
"Bijun Li"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Zeng_DeepLA-Net_Very_Deep_Local_Aggregation_Networks_for_Point_Cloud_Analysis_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Zeng_DeepLA-Net_Very_Deep_Local_Aggregation_Networks_for_Point_Cloud_Analysis_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zeng_DeepLA-Net_Very_Deep_CVPR_2025_supplemental.pdf | null | @InProceedings{Zeng_2025_CVPR,
author = {Zeng, Ziyin and Dong, Mingyue and Zhou, Jian and Qiu, Huan and Dong, Zhen and Luo, Man and Li, Bijun},
title = {DeepLA-Net: Very Deep Local Aggregation Networks for Point Cloud Analysis},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {1330-1341}
} | Due to the irregular and disordered data structure in 3D point clouds, prior works have focused on designing more sophisticated local representation methods to capture these complex local patterns. However, the recognition performance has saturated over the past few years, indicating that increasingly complex and redundant designs no longer make improvements to local learning. This phenomenon prompts us to diverge from the trend in 3D vision and instead pursue an alternative and successful solution: deeper neural networks. In this paper, we propose DeepLA-Net, a series of very deep networks for point cloud analysis. The key insight of our approach is to exploit a small but mighty local learning block, which reduces 10xfewer FLOPs, enabling the construction of very deep networks. Furthermore, we design a training supervision strategy to ensure smooth gradient backpropagation and optimization in very deep networks. We construct the DeepLA-Net family with a depth of up to 120 blocks --- at least 5xdeeper than recent methods --- trained on a single RTX 3090. An ensemble of the DeepLA-Net achieves state-of-the-art performance on classification and segmentation tasks of S3DIS Area5 (+2.2% mIoU), ScanNet test set (+1.6% mIoU), ScanObjectNN (+2.1% OA), and ShapeNetPart (+0.9% cls.mIoU). | [
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] |
15 | Multi-Layer Visual Feature Fusion in Multimodal LLMs: Methods, Analysis, and Best Practices | [
"Junyan Lin",
"Haoran Chen",
"Yue Fan",
"Yingqi Fan",
"Xin Jin",
"Hui Su",
"Jinlan Fu",
"Xiaoyu Shen"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Lin_Multi-Layer_Visual_Feature_Fusion_in_Multimodal_LLMs_Methods_Analysis_and_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Lin_Multi-Layer_Visual_Feature_Fusion_in_Multimodal_LLMs_Methods_Analysis_and_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lin_Multi-Layer_Visual_Feature_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.06063 | @InProceedings{Lin_2025_CVPR,
author = {Lin, Junyan and Chen, Haoran and Fan, Yue and Fan, Yingqi and Jin, Xin and Su, Hui and Fu, Jinlan and Shen, Xiaoyu},
title = {Multi-Layer Visual Feature Fusion in Multimodal LLMs: Methods, Analysis, and Best Practices},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {4156-4166}
} | Multimodal Large Language Models (MLLMs) have made significant advancements in recent years, with visual features playing an increasingly critical role in enhancing model performance. However, the integration of multi-layer visual features in MLLMs remains underexplored, particularly with regard to optimal layer selection and fusion strategies. Existing methods often rely on arbitrary design choices, leading to suboptimal outcomes. In this paper, we systematically investigate two core aspects of multi-layer visual feature fusion: (1) selecting the most effective visual layers and (2) identifying the best fusion approach with the language model. Our experiments reveal that while combining visual features from multiple stages improves generalization, incorporating additional features from the same stage typically leads to diminished performance. Furthermore, we find that direct fusion of multi-layer visual features at the input stage consistently yields superior and more stable performance across various configurations. | [
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16 | APHQ-ViT: Post-Training Quantization with Average Perturbation Hessian Based Reconstruction for Vision Transformers | [
"Zhuguanyu Wu",
"Jiayi Zhang",
"Jiaxin Chen",
"Jinyang Guo",
"Di Huang",
"Yunhong Wang"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Wu_APHQ-ViT_Post-Training_Quantization_with_Average_Perturbation_Hessian_Based_Reconstruction_for_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_APHQ-ViT_Post-Training_Quantization_with_Average_Perturbation_Hessian_Based_Reconstruction_for_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_APHQ-ViT_Post-Training_Quantization_CVPR_2025_supplemental.pdf | null | @InProceedings{Wu_2025_CVPR,
author = {Wu, Zhuguanyu and Zhang, Jiayi and Chen, Jiaxin and Guo, Jinyang and Huang, Di and Wang, Yunhong},
title = {APHQ-ViT: Post-Training Quantization with Average Perturbation Hessian Based Reconstruction for Vision Transformers},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {9686-9695}
} | Vision Transformers (ViTs) have become one of the most commonly used backbones for vision tasks. Despite their remarkable performance, they often suffer significant accuracy drop when quantized for practical deployment, particularly by post-training quantization (PTQ) under ultra-low bits. Recently, reconstruction-based PTQ methods have shown promising performance in quantizing Convolutional Neural Networks (CNNs). However, they fail when applied to ViTs, primarily due to the inaccurate estimation of output importance and the substantial accuracy degradation in quantizing post-GELU activations. To address these issues, we propose APHQ-ViT, a novel PTQ approach based on importance estimation with Average Perturbation Hessian (APH). Specifically, we first thoroughly analyze the current approximation approaches with Hessian loss, and propose an improved average perturbation Hessian loss. To deal with the quantization of the post-GELU activations, we design an MLP-Reconstruction (MR) method by replacing the GELU function in MLP with ReLU and reconstructing it by the APH loss on a small unlabeled calibration set. Extensive experiments demonstrate that APHQ-ViT using linear quantizers outperforms existing PTQ methods by substantial margins in 3-bit and 4-bit across different vision tasks. The source code is available at https://github.com/GoatWu/APHQ-ViT. | [
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] |
17 | AdaptCMVC: Robust Adaption to Incremental Views in Continual Multi-view Clustering | [
"Jing Wang",
"Songhe Feng",
"Kristoffer Knutsen Wickstrøm",
"Michael C. Kampffmeyer"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Wang_AdaptCMVC_Robust_Adaption_to_Incremental_Views_in_Continual_Multi-view_Clustering_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_AdaptCMVC_Robust_Adaption_to_Incremental_Views_in_Continual_Multi-view_Clustering_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_AdaptCMVC_Robust_Adaption_CVPR_2025_supplemental.pdf | null | @InProceedings{Wang_2025_CVPR,
author = {Wang, Jing and Feng, Songhe and Wickstr{\o}m, Kristoffer Knutsen and Kampffmeyer, Michael C.},
title = {AdaptCMVC: Robust Adaption to Incremental Views in Continual Multi-view Clustering},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {10285-10294}
} | Most Multi-view Clustering approaches assume that all views are available for clustering. However, this assumption is often unrealistic as views are incrementally accumulated over time, leading to a need for continual multi-view clustering (CMVC) methods. Current approaches to CMVC leverage late fusion-based approaches, where a new model is typically learned individually for each view to obtain the corresponding partition matrix, and then used to update a consensus matrix via a moving average. These approaches are prone to view-specific noise and struggle to adapt to large gaps between different views. To address these shortcomings, we reconsider CMVC from the perspective of domain adaptation and propose AdaptCMVC, which learns how to incrementally accumulate knowledge of new views as they become available and prevents catastrophic forgetting. Specifically, a self-training framework is introduced to extend the model to new views, particularly designed to be robust to view-specific noise. Further, to combat catastrophic forgetting, a structure alignment mechanism is proposed to enable the model to explore the global group structure across multiple views. Experiments on several multi-view benchmarks demonstrate the effectiveness of our proposed method on the CMVC task. The code is available at: AdaptCMVC. | [
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18 | Omni-Scene: Omni-Gaussian Representation for Ego-Centric Sparse-View Scene Reconstruction | [
"Dongxu Wei",
"Zhiqi Li",
"Peidong Liu"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Wei_Omni-Scene_Omni-Gaussian_Representation_for_Ego-Centric_Sparse-View_Scene_Reconstruction_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Wei_Omni-Scene_Omni-Gaussian_Representation_for_Ego-Centric_Sparse-View_Scene_Reconstruction_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wei_Omni-Scene_Omni-Gaussian_Representation_CVPR_2025_supplemental.zip | null | @InProceedings{Wei_2025_CVPR,
author = {Wei, Dongxu and Li, Zhiqi and Liu, Peidong},
title = {Omni-Scene: Omni-Gaussian Representation for Ego-Centric Sparse-View Scene Reconstruction},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {22317-22327}
} | Prior works employing pixel-based Gaussian representation have demonstrated efficacy in feed-forward sparse-view reconstruction. However, such representation necessitates cross-view overlap for accurate depth estimation, and is challenged by object occlusions and frustum truncations. As a result, these methods require scene-centric data acquisition to maintain cross-view overlap and complete scene visibility to circumvent occlusions and truncations, which limits their applicability to scene-centric reconstruction. In contrast, in autonomous driving scenarios, a more practical paradigm is ego-centric reconstruction, which is characterized by minimal cross-view overlap and frequent occlusions and truncations. The limitations of pixel-based representation thus hinder the utility of prior works in this task. In light of this, this paper conducts an in-depth analysis of different representations, and introduces Omni-Gaussian representation with tailored network design to complement their strengths and mitigate their drawbacks. Experiments show that our method significantly surpasses state-of-the-art methods, pixelSplat and MVSplat, in ego-centric reconstruction, and achieves comparable performance to prior works in scene-centric reconstruction. Our code is available at https://github.com/WU-CVGL/Omni-Scene. | [
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19 | 3DTopia-XL: Scaling High-quality 3D Asset Generation via Primitive Diffusion | [
"Zhaoxi Chen",
"Jiaxiang Tang",
"Yuhao Dong",
"Ziang Cao",
"Fangzhou Hong",
"Yushi Lan",
"Tengfei Wang",
"Haozhe Xie",
"Tong Wu",
"Shunsuke Saito",
"Liang Pan",
"Dahua Lin",
"Ziwei Liu"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Chen_3DTopia-XL_Scaling_High-quality_3D_Asset_Generation_via_Primitive_Diffusion_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_3DTopia-XL_Scaling_High-quality_3D_Asset_Generation_via_Primitive_Diffusion_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_3DTopia-XL_Scaling_High-quality_CVPR_2025_supplemental.pdf | null | @InProceedings{Chen_2025_CVPR,
author = {Chen, Zhaoxi and Tang, Jiaxiang and Dong, Yuhao and Cao, Ziang and Hong, Fangzhou and Lan, Yushi and Wang, Tengfei and Xie, Haozhe and Wu, Tong and Saito, Shunsuke and Pan, Liang and Lin, Dahua and Liu, Ziwei},
title = {3DTopia-XL: Scaling High-quality 3D Asset Generation via Primitive Diffusion},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {26576-26586}
} | The increasing demand for high-quality 3D assets across various industries necessitates efficient and automated 3D content creation. Despite recent advancements in 3D generative models, existing methods still face challenges with optimization speed, geometric fidelity, and the lack of assets for physically based rendering (PBR). In this paper, we introduce 3DTopia-XL, a scalable native 3D generative model designed to overcome these limitations. 3DTopia-XL leverages a novel primitive-based 3D representation, PrimX, which encodes detailed shape, albedo, and material field into a compact tensorial format, facilitating the modeling of high-resolution geometry with PBR assets. On top of the novel representation, we propose a generative framework based on Diffusion Transformer (DiT), which comprises 1) Primitive Patch Compression, 2) and Latent Primitive Diffusion. 3DTopia-XL learns to generate high-quality 3D assets from textual or visual inputs. Extensive qualitative and quantitative experiments are conducted to demonstrate that 3DTopia-XL significantly outperforms existing methods in generating high-quality 3D assets with fine-grained textures and materials, efficiently bridging the quality gap between generative models and real-world applications. | [
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20 | UA-Pose: Uncertainty-Aware 6D Object Pose Estimation and Online Object Completion with Partial References | [
"Ming-Feng Li",
"Xin Yang",
"Fu-En Wang",
"Hritam Basak",
"Yuyin Sun",
"Shreekant Gayaka",
"Min Sun",
"Cheng-Hao Kuo"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Li_UA-Pose_Uncertainty-Aware_6D_Object_Pose_Estimation_and_Online_Object_Completion_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Li_UA-Pose_Uncertainty-Aware_6D_Object_Pose_Estimation_and_Online_Object_Completion_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_UA-Pose_Uncertainty-Aware_6D_CVPR_2025_supplemental.pdf | null | @InProceedings{Li_2025_CVPR,
author = {Li, Ming-Feng and Yang, Xin and Wang, Fu-En and Basak, Hritam and Sun, Yuyin and Gayaka, Shreekant and Sun, Min and Kuo, Cheng-Hao},
title = {UA-Pose: Uncertainty-Aware 6D Object Pose Estimation and Online Object Completion with Partial References},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {1180-1189}
} | 6D object pose estimation has shown strong generalizability to novel objects. However, existing methods often require either a complete, well-reconstructed 3D model or numerous reference images that fully cover the object. Estimating 6D poses from partial references, which capture only fragments of an object's appearance and geometry, remains challenging. To address this, we propose UA-Pose, an uncertainty-aware approach for 6D object pose estimation and online object completion specifically designed for partial references. We assume access to either (1) a limited set of RGBD images with known poses or (2) a single 2D image. For the first case, we initialize a partial object 3D model based on the provided images and poses, while for the second, we use image-to-3D techniques to generate an initial object 3D model. Our method integrates uncertainty into the incomplete 3D model, distinguishing between seen and unseen regions. This uncertainty enables confidence assessment in pose estimation and guides an uncertainty-aware sampling strategy for online object completion, enhancing robustness in pose estimation accuracy and improving object completeness. We evaluate our method on the YCB-Video, YCBInEOAT, and HO3D datasets, including RGBD sequences of YCB objects manipulated by robots and human hands. Experimental results demonstrate significant performance improvements over existing methods, particularly when object observations are incomplete or partially captured. | [
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21 | Missing Target-Relevant Information Prediction with World Model for Accurate Zero-Shot Composed Image Retrieval | [
"Yuanmin Tang",
"Jing Yu",
"Keke Gai",
"Jiamin Zhuang",
"Gang Xiong",
"Gaopeng Gou",
"Qi Wu"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Tang_Missing_Target-Relevant_Information_Prediction_with_World_Model_for_Accurate_Zero-Shot_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Tang_Missing_Target-Relevant_Information_Prediction_with_World_Model_for_Accurate_Zero-Shot_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tang_Missing_Target-Relevant_Information_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.17109 | @InProceedings{Tang_2025_CVPR,
author = {Tang, Yuanmin and Yu, Jing and Gai, Keke and Zhuang, Jiamin and Xiong, Gang and Gou, Gaopeng and Wu, Qi},
title = {Missing Target-Relevant Information Prediction with World Model for Accurate Zero-Shot Composed Image Retrieval},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {24785-24795}
} | Zero-Shot Composed Image Retrieval (ZS-CIR) involves diverse tasks with a broad range of visual content manipulation intent across domain, scene, object, and attribute. The key challenge for ZS-CIR tasks is to modify a reference image according to manipulation text to accurately retrieve a target image, especially when the reference image is missing essential target content. In this paper, we propose a novel prediction-based mapping network, named PrediCIR, to adaptively predict the missing target visual content in reference images in the latent space before mapping for accurate ZS-CIR. Specifically, a world view generation module first constructs a source view by omitting certain visual content of a target view, coupled with an action that includes the manipulation intent derived from existing image-caption pairs. Then, a target content prediction module trains a world model as a predictor to adaptively predict the missing visual information guided by user intention in manipulating text at the latent space. The two modules map an image with the predicted relevant information to a pseudo-word token without extra supervision. Our model shows strong generalization ability on six ZS-CIR tasks. It obtains consistent and significant performance boosts ranging from 1.73% to 4.45% over the best methods and achieves new state-of-the-art results on ZS-CIR. Our code is available at https://github.com/Pter61/predicir. | [
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] |
22 | Binarized Mamba-Transformer for Lightweight Quad Bayer HybridEVS Demosaicing | [
"Shiyang Zhou",
"Haijin Zeng",
"Yunfan Lu",
"Tong Shao",
"Ke Tang",
"Yongyong Chen",
"Jie Liu",
"Jingyong Su"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_Binarized_Mamba-Transformer_for_Lightweight_Quad_Bayer_HybridEVS_Demosaicing_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_Binarized_Mamba-Transformer_for_Lightweight_Quad_Bayer_HybridEVS_Demosaicing_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_Binarized_Mamba-Transformer_for_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.16134 | @InProceedings{Zhou_2025_CVPR,
author = {Zhou, Shiyang and Zeng, Haijin and Lu, Yunfan and Shao, Tong and Tang, Ke and Chen, Yongyong and Liu, Jie and Su, Jingyong},
title = {Binarized Mamba-Transformer for Lightweight Quad Bayer HybridEVS Demosaicing},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {8817-8827}
} | Quad Bayer demosaicing is the central challenge for enabling the widespread application of Hybrid Event-based Vision Sensors (HybridEVS). Although existing learning-based methods that leverage long-range dependency modeling have achieved promising results, their complexity severely limits deployment on mobile devices for real-world applications. To address these limitations, we propose a lightweight Mamba-based binary neural network designed for efficient and high-performing demosaicing of HybridEVS RAW images. First, to effectively capture both global and local dependencies, we introduce a hybrid Binarized Mamba-Transformer architecture that combines the strengths of the Mamba and Swin Transformer architectures. Next, to significantly reduce computational complexity, we propose a binarized Mamba (Bi-Mamba), which binarizes all projections while retaining the core Selective Scan in full precision. Bi-Mamba also incorporates additional global visual information to enhance global context and mitigate precision loss. We conduct quantitative and qualitative experiments to demonstrate the effectiveness of BMTNet in both performance and computational efficiency, providing a lightweight demosaicing solution suited for real-world edge devices. Our codes and models are available at https://github.com/Clausy9/BMTNet. | [
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23 | DiffSensei: Bridging Multi-Modal LLMs and Diffusion Models for Customized Manga Generation | [
"Jianzong Wu",
"Chao Tang",
"Jingbo Wang",
"Yanhong Zeng",
"Xiangtai Li",
"Yunhai Tong"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Wu_DiffSensei_Bridging_Multi-Modal_LLMs_and_Diffusion_Models_for_Customized_Manga_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_DiffSensei_Bridging_Multi-Modal_LLMs_and_Diffusion_Models_for_Customized_Manga_CVPR_2025_paper.pdf | null | http://arxiv.org/abs/2412.07589 | @InProceedings{Wu_2025_CVPR,
author = {Wu, Jianzong and Tang, Chao and Wang, Jingbo and Zeng, Yanhong and Li, Xiangtai and Tong, Yunhai},
title = {DiffSensei: Bridging Multi-Modal LLMs and Diffusion Models for Customized Manga Generation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {28684-28693}
} | Story visualization, the task of creating visual narratives from textual descriptions, has seen progress with text-to-image generation models. However, these models often lack effective control over character appearances and interactions, particularly in multi-character scenes. To address these limitations, we propose a new task: customized manga generation and introduce DiffSensei, an innovative framework specifically designed for generating manga with dynamic multi-character control. DiffSensei integrates a diffusion-based image generator with a multimodal large language model (MLLM) that acts as a text-compatible identity adapter. Our approach employs masked cross-attention to seamlessly incorporate character features, enabling precise layout control without direct pixel transfer. Additionally, the MLLM-based adapter adjusts character features to align with panel-specific text cues, allowing flexible adjustments in character expressions, poses, and actions. We also introduce MangaZero, a large-scale dataset tailored to this task, containing 43,264 manga pages and 427,147 annotated panels, supporting the visualization of varied character interactions and movements across sequential frames. Extensive experiments demonstrate that DiffSensei outperforms existing models, marking a significant advancement in manga generation by enabling text-adaptable character customization. The code, model, and dataset will be open-sourced to the community. | [
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24 | Narrating the Video: Boosting Text-Video Retrieval via Comprehensive Utilization of Frame-Level Captions | [
"Chan Hur",
"Jeong-hun Hong",
"Dong-hun Lee",
"Dabin Kang",
"Semin Myeong",
"Sang-hyo Park",
"Hyeyoung Park"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Hur_Narrating_the_Video_Boosting_Text-Video_Retrieval_via_Comprehensive_Utilization_of_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Hur_Narrating_the_Video_Boosting_Text-Video_Retrieval_via_Comprehensive_Utilization_of_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hur_Narrating_the_Video_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.05186 | @InProceedings{Hur_2025_CVPR,
author = {Hur, Chan and Hong, Jeong-hun and Lee, Dong-hun and Kang, Dabin and Myeong, Semin and Park, Sang-hyo and Park, Hyeyoung},
title = {Narrating the Video: Boosting Text-Video Retrieval via Comprehensive Utilization of Frame-Level Captions},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {24077-24086}
} | In recent text-video retrieval, the use of additional captions from vision-language models has shown promising effects on the performance. However, existing models using additional captions often have struggled to capture the rich semantics, including temporal changes, inherent in the video. In addition, incorrect information caused by generative models can lead to inaccurate retrieval. To address these issues, we propose a new framework, Narrating the Video (NarVid), which strategically leverages the comprehensive information available from frame-level captions, the narration. The proposed NarVid exploits narration in multiple ways: 1) feature enhancement through cross-modal interactions between narration and video, 2) query-aware adaptive filtering to suppress irrelevant or incorrect information, 3) dual-modal matching score by adding query-video similarity and query-narration similarity, and 4) hard-negative loss to learn discriminative features from multiple perspectives using the two similarities from different views. Experimental results demonstrate that NarVid achieves state-of-the-art performance on various benchmark datasets. | [
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] |
25 | IDEA-Bench: How Far are Generative Models from Professional Designing? | [
"Chen Liang",
"Lianghua Huang",
"Jingwu Fang",
"Huanzhang Dou",
"Wei Wang",
"Zhi-Fan Wu",
"Yupeng Shi",
"Junge Zhang",
"Xin Zhao",
"Yu Liu"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Liang_IDEA-Bench_How_Far_are_Generative_Models_from_Professional_Designing_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Liang_IDEA-Bench_How_Far_are_Generative_Models_from_Professional_Designing_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liang_IDEA-Bench_How_Far_CVPR_2025_supplemental.pdf | null | @InProceedings{Liang_2025_CVPR,
author = {Liang, Chen and Huang, Lianghua and Fang, Jingwu and Dou, Huanzhang and Wang, Wei and Wu, Zhi-Fan and Shi, Yupeng and Zhang, Junge and Zhao, Xin and Liu, Yu},
title = {IDEA-Bench: How Far are Generative Models from Professional Designing?},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {18541-18551}
} | Recent advancements in image generation models enable the creation of high-quality images and targeted modifications based on textual instructions. Some models even support multimodal complex guidance and demonstrate robust task generalization capabilities. However, they still fall short of meeting the nuanced, professional demands of designers. To bridge this gap, we introduce IDEA-Bench, a comprehensive benchmark designed to advance image generation models toward applications with robust task generalization. IDEA-Bench comprises 100 professional image generation tasks and 275 specific cases, categorized into five major types based on the current capabilities of existing models. Furthermore, we provide a representative subset of 18 tasks with enhanced evaluation criteria to facilitate more nuanced and reliable evaluations using Multimodal Large Language Models (MLLMs). By assessing models' ability to comprehend and execute novel, complex tasks, IDEA-Bench paves the way toward the development of generative models with autonomous and versatile visual generation capabilities. | [
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] |
26 | Interpretable Image Classification via Non-parametric Part Prototype Learning | [
"Zhijie Zhu",
"Lei Fan",
"Maurice Pagnucco",
"Yang Song"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_Interpretable_Image_Classification_via_Non-parametric_Part_Prototype_Learning_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_Interpretable_Image_Classification_via_Non-parametric_Part_Prototype_Learning_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhu_Interpretable_Image_Classification_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.10247 | @InProceedings{Zhu_2025_CVPR,
author = {Zhu, Zhijie and Fan, Lei and Pagnucco, Maurice and Song, Yang},
title = {Interpretable Image Classification via Non-parametric Part Prototype Learning},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {9762-9771}
} | Classifying images with an interpretable decision-making process is a long-standing problem in computer vision. In recent years, Prototypical Part Networks has gained traction as an approach for self-explainable neural networks, due to their ability to mimic human visual reasoning by providing explanations based on prototypical object parts. However, the quality of the explanations generated by these methods leaves room for improvement, as the prototypes usually focus on repetitive and redundant concepts. Leveraging recent advances in prototype learning, we present a framework for part-based interpretable image classification that learns a set of semantically distinctive object parts for each class, and provides diverse and comprehensive explanations. The core of our method is to learn the part-prototypes in a non-parametric fashion, through clustering deep features extracted from foundation vision models that encode robust semantic information. To quantitatively evaluate the quality of explanations provided by ProtoPNets, we introduce Distinctiveness Score and Comprehensiveness Score. Through evaluation on CUB-200-2011, Stanford Cars and Stanford Dogs datasets, we show that our framework compares favourably against existing ProtoPNets while achieving better interpretability. | [
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] |
27 | PhD: A ChatGPT-Prompted Visual Hallucination Evaluation Dataset | [
"Jiazhen Liu",
"Yuhan Fu",
"Ruobing Xie",
"Runquan Xie",
"Xingwu Sun",
"Fengzong Lian",
"Zhanhui Kang",
"Xirong Li"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Liu_PhD_A_ChatGPT-Prompted_Visual_Hallucination_Evaluation_Dataset_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_PhD_A_ChatGPT-Prompted_Visual_Hallucination_Evaluation_Dataset_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_PhD_A_ChatGPT-Prompted_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2403.11116 | @InProceedings{Liu_2025_CVPR,
author = {Liu, Jiazhen and Fu, Yuhan and Xie, Ruobing and Xie, Runquan and Sun, Xingwu and Lian, Fengzong and Kang, Zhanhui and Li, Xirong},
title = {PhD: A ChatGPT-Prompted Visual Hallucination Evaluation Dataset},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {19857-19866}
} | Multimodal Large Language Models (MLLMs) hallucinate, resulting in an emerging topic of visual hallucination evaluation (VHE). This paper contributes a ChatGPT-Prompted visual hallucination evaluation Dataset (PhD) for objective VHE at a large scale. The essence of VHE is to ask an MLLM questions about specific images to assess its susceptibility to hallucination. Depending on what to ask (objects, attributes, sentiment, etc.) and how the questions are asked, we structure PhD along two dimensions, i.e. task and mode. Five visual recognition tasks, ranging from low-level (object / attribute recognition) to middle-level (sentiment / position recognition and counting), are considered. Besides a normal visual QA mode, which we term PhD-base, PhD also asks questions with specious context (PhD-sec) or with incorrect context (PhD-icc), or with AI-generated counter common sense images (PhD-ccs). We construct PhD by a ChatGPT-assisted semi-automated pipeline, encompassing four pivotal modules: task-specific hallucinatory item (hitem) selection, hitem-embedded question generation, specious / incorrect context generation, and counter-common-sense (CCS) image generation. With over 14k daily images, 750 CCS images and 102k VQA triplets in total, PhD reveals considerable variability in MLLMs' performance across various modes and tasks, offering valuable insights into the nature of hallucination. As such, PhD stands as a potent tool not only for VHE but may also play a significant role in the refinement of MLLMs. | [
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28 | CARL: A Framework for Equivariant Image Registration | [
"Hastings Greer",
"Lin Tian",
"François-Xavier Vialard",
"Roland Kwitt",
"Raul San Jose Estepar",
"Marc Niethammer"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Greer_CARL_A_Framework_for_Equivariant_Image_Registration_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Greer_CARL_A_Framework_for_Equivariant_Image_Registration_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Greer_CARL_A_Framework_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2405.16738 | @InProceedings{Greer_2025_CVPR,
author = {Greer, Hastings and Tian, Lin and Vialard, Fran\c{c}ois-Xavier and Kwitt, Roland and Estepar, Raul San Jose and Niethammer, Marc},
title = {CARL: A Framework for Equivariant Image Registration},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {26014-26023}
} | Image registration estimates spatial correspondences between image pairs. These estimates are typically obtained via numerical optimization or regression by a deep network. A desirable property is that a correspondence estimate (e.g., the true oracle correspondence) for an image pair is maintained under deformations of the input images. Formally, the estimator should be equivariant to a desired class of image transformations. In this work, we present careful analyses of equivariance properties in the context of multi-step deep registration networks. Based on these analyses we 1) introduce the notions of [U,U] equivariance (network equivariance to the same deformations of the input images) and [W,U] equivariance (where input images can undergo different deformations); we 2) show that in a suitable multi-step registration setup it is sufficient for overall [W,U] equivariance if the first step has [W,U] equivariance and all others have [U,U] equivariance; we 3) show that common displacement-predicting networks only exhibit [U,U] equivariance to translations instead of the more powerful [W,U] equivariance; and we 4) show how to achieve multi-step [W,U] equivariance via a coordinate-attention mechanism combined with displacement-predicting networks. Our approach obtains excellent practical performance for 3D abdomen, lung, and brain medical image registration. We match or outperform state-of-the-art (SOTA) registration approaches on all the datasets with a particularly strong performance for the challening abdomen registration. | [
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] |
29 | ClimbingCap: Multi-Modal Dataset and Method for Rock Climbing in World Coordinate | [
"Ming Yan",
"Xincheng Lin",
"Yuhua Luo",
"Shuqi Fan",
"Yudi Dai",
"Qixin Zhong",
"Lincai Zhong",
"Yuexin Ma",
"Lan Xu",
"Chenglu Wen",
"Siqi Shen",
"Cheng Wang"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Yan_ClimbingCap_Multi-Modal_Dataset_and_Method_for_Rock_Climbing_in_World_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_ClimbingCap_Multi-Modal_Dataset_and_Method_for_Rock_Climbing_in_World_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yan_ClimbingCap_Multi-Modal_Dataset_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.21268 | @InProceedings{Yan_2025_CVPR,
author = {Yan, Ming and Lin, Xincheng and Luo, Yuhua and Fan, Shuqi and Dai, Yudi and Zhong, Qixin and Zhong, Lincai and Ma, Yuexin and Xu, Lan and Wen, Chenglu and Shen, Siqi and Wang, Cheng},
title = {ClimbingCap: Multi-Modal Dataset and Method for Rock Climbing in World Coordinate},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {12312-12323}
} | Human Motion Recovery (HMR) research mainly focuses on ground-based motions such as running. The study on capturing climbing motion, an off-ground motion, is sparse. This is partly due to the limited availability of climbing motion datasets, especially large-scale and challenging 3D labeled datasets. To address the insufficiency of climbing motion datasets, we collect AscendMotion, a large-scale well-annotated, and challenging climbing motion dataset. It consists of 412k RGB, LiDAR frames, and IMU measurements, which includes the challenging climbing motions of 22 professional climbing coaches across 12 different rocks. Capturing the climbing motions is challenging as it requires precise recovery of not only the complex pose but also the global position of climbers. Although multiple global HMR methods have been proposed, they cannot faithfully capture climbing motions. To address the limitations of HMR methods for climbing, we propose ClimbingCap, a motion recovery method that reconstructs continuous 3D human climbing motion in a global coordinate system. One key insight is to use the RGB and the LiDAR modalities to separately reconstruct motions in camera coordinates and global coordinates and optimize them jointly. We demonstrate the quality of the AscendMotion dataset and present promising results from ClimbingCap. The AscendMotion dataset and the source code of ClimbingCap will be released publicly to the research community. | [
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] |
30 | DAGSM: Disentangled Avatar Generation with GS-enhanced Mesh | [
"Jingyu Zhuang",
"Di Kang",
"Linchao Bao",
"Liang Lin",
"Guanbin Li"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Zhuang_DAGSM_Disentangled_Avatar_Generation_with_GS-enhanced_Mesh_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Zhuang_DAGSM_Disentangled_Avatar_Generation_with_GS-enhanced_Mesh_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhuang_DAGSM_Disentangled_Avatar_CVPR_2025_supplemental.zip | http://arxiv.org/abs/2411.15205 | @InProceedings{Zhuang_2025_CVPR,
author = {Zhuang, Jingyu and Kang, Di and Bao, Linchao and Lin, Liang and Li, Guanbin},
title = {DAGSM: Disentangled Avatar Generation with GS-enhanced Mesh},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {292-303}
} | Text-driven avatar generation has gained significant attention owing to its convenience. However, existing methods typically model the human body with all garments as a single 3D model, limiting its usability, such as clothing replacement, and reducing user control over the generation process. To overcome the limitations above, we propose DAGSM, a novel pipeline that generates disentangled human bodies and garments from the given text prompts. Specifically, we model each part (e.g., body, upper/lower clothes) of the clothed human as one GS-enhanced mesh (GSM), which is a traditional mesh attached with 2D Gaussians to better handle complicated textures (e.g., woolen, translucent clothes) and produce realistic cloth animations. During the generation, we first create the unclothed body, followed by a sequence of individual cloth generation based on the body, where we introduce a semantic-based algorithm to achieve better human-cloth and garment-garment separation. To improve texture quality, we propose a view-consistent texture refinement module, including a cross-view attention mechanism for texture style consistency and an incident-angle-weighted denoising (IAW-DE) strategy to update the appearance. Extensive experiments have demonstrated that DAGSM generates high-quality disentangled avatars, supports clothing replacement and realistic animation, and outperforms the baselines in visual quality. | [
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] |
31 | Estimating Body and Hand Motion in an Ego-sensed World | [
"Brent Yi",
"Vickie Ye",
"Maya Zheng",
"Yunqi Li",
"Lea Müller",
"Georgios Pavlakos",
"Yi Ma",
"Jitendra Malik",
"Angjoo Kanazawa"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Yi_Estimating_Body_and_Hand_Motion_in_an_Ego-sensed_World_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Yi_Estimating_Body_and_Hand_Motion_in_an_Ego-sensed_World_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yi_Estimating_Body_and_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2410.03665 | @InProceedings{Yi_2025_CVPR,
author = {Yi, Brent and Ye, Vickie and Zheng, Maya and Li, Yunqi and M\"uller, Lea and Pavlakos, Georgios and Ma, Yi and Malik, Jitendra and Kanazawa, Angjoo},
title = {Estimating Body and Hand Motion in an Ego-sensed World},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {7072-7084}
} | We present EgoAllo, a system for human motion estimation from a head-mounted device. Using only egocentric SLAM poses and images, EgoAllo guides sampling from a conditional diffusion model to estimate 3D body pose, height, and hand parameters that capture a device wearer's actions in the allocentric coordinate frame of the scene. To achieve this, our key insight is in representation: we propose spatial and temporal invariance criteria for improving model performance, from which we derive a head motion conditioning parameterization that improves estimation by up to 18%. We also show how the bodies estimated by our system can improve hand estimation: the resulting kinematic and temporal constraints can reduce world-frame errors in single-frame estimates by 40%. | [
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32 | A Bias-Free Training Paradigm for More General AI-generated Image Detection | [
"Fabrizio Guillaro",
"Giada Zingarini",
"Ben Usman",
"Avneesh Sud",
"Davide Cozzolino",
"Luisa Verdoliva"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Guillaro_A_Bias-Free_Training_Paradigm_for_More_General_AI-generated_Image_Detection_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Guillaro_A_Bias-Free_Training_Paradigm_for_More_General_AI-generated_Image_Detection_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Guillaro_A_Bias-Free_Training_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2412.17671 | @InProceedings{Guillaro_2025_CVPR,
author = {Guillaro, Fabrizio and Zingarini, Giada and Usman, Ben and Sud, Avneesh and Cozzolino, Davide and Verdoliva, Luisa},
title = {A Bias-Free Training Paradigm for More General AI-generated Image Detection},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {18685-18694}
} | Successful forensic detectors can produce excellent results in supervised learning benchmarks but struggle to transfer to real-world applications. We believe this limitation is largely due to inadequate training data quality. While most research focuses on developing new algorithms, less attention is given to training data selection, despite evidence that performance can be strongly impacted by spurious correlations such as content, format, or resolution. A well-designed forensic detector should detect generator specific artifacts rather than reflect data biases. To this end, we propose B-Free, a bias-free training paradigm, where fake images are generated from real ones using the conditioning procedure of stable diffusion models. This ensures semantic alignment between real and fake images, allowing any differences to stem solely from the subtle artifacts introduced by AI generation. Through content-based augmentation, we show significant improvements in both generalization and robustness over state-of-the-art detectors and more calibrated results across 27 different generative models, including recent releases, like FLUX and Stable Diffusion 3.5. Our findings emphasize the importance of a careful dataset design, highlighting the need for further research on this topic. Code and data are publicly available at https://grip-unina.github.io/B-Free/. | [
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] |
33 | FALCON: Fairness Learning via Contrastive Attention Approach to Continual Semantic Scene Understanding | [
"Thanh-Dat Truong",
"Utsav Prabhu",
"Bhiksha Raj",
"Jackson Cothren",
"Khoa Luu"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Truong_FALCON_Fairness_Learning_via_Contrastive_Attention_Approach_to_Continual_Semantic_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Truong_FALCON_Fairness_Learning_via_Contrastive_Attention_Approach_to_Continual_Semantic_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Truong_FALCON_Fairness_Learning_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2311.15965 | @InProceedings{Truong_2025_CVPR,
author = {Truong, Thanh-Dat and Prabhu, Utsav and Raj, Bhiksha and Cothren, Jackson and Luu, Khoa},
title = {FALCON: Fairness Learning via Contrastive Attention Approach to Continual Semantic Scene Understanding},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {15065-15075}
} | Continual Learning in semantic scene segmentation aims to continually learn new unseen classes in dynamic environments while maintaining previously learned knowledge. Prior studies focused on modeling the catastrophic forgetting and background shift challenges in continual learning. However, fairness, another major challenge that causes unfair predictions leading to low performance among major and minor classes, still needs to be well addressed. In addition, prior methods have yet to model the unknown classes well, thus resulting in producing non-discriminative features among unknown classes. This work presents a novel Fairness Learning via Contrastive Attention Approach to continual learning in semantic scene understanding. In particular, we first introduce a new Fairness Contrastive Clustering loss to address the problems of catastrophic forgetting and fairness. Then, we propose an attention-based visual grammar approach to effectively model the background shift problem and unknown classes, producing better feature representations for different unknown classes. Through our experiments, our proposed approach achieves State-of-the-Art (SoTA) performance on different continual learning benchmarks, i.e., ADE20K, Cityscapes, and Pascal VOC. It promotes the fairness of the continual semantic segmentation model. | [
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] |
34 | Certified Human Trajectory Prediction | [
"Mohammadhossein Bahari",
"Saeed Saadatnejad",
"Amirhossein Askari Farsangi",
"Seyed-Mohsen Moosavi-Dezfooli",
"Alexandre Alahi"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Bahari_Certified_Human_Trajectory_Prediction_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Bahari_Certified_Human_Trajectory_Prediction_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bahari_Certified_Human_Trajectory_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2403.13778 | @InProceedings{Bahari_2025_CVPR,
author = {Bahari, Mohammadhossein and Saadatnejad, Saeed and Farsangi, Amirhossein Askari and Moosavi-Dezfooli, Seyed-Mohsen and Alahi, Alexandre},
title = {Certified Human Trajectory Prediction},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {12301-12311}
} | Predicting human trajectories is essential for the safe operation of autonomous vehicles, yet current data-driven models often lack robustness in case of noisy inputs such as adversarial examples or imperfect observations. Although some trajectory prediction methods have been developed to provide empirical robustness, these methods are heuristic and do not offer guaranteed robustness. In this work, we propose a certification approach tailored for trajectory prediction that provides guaranteed robustness. To this end, we address the unique challenges associated with trajectory prediction, such as unbounded outputs and multi-modality. To mitigate the inherent performance drop through certification, we propose a diffusion-based trajectory denoiser and integrate it into our method. Moreover, we introduce new certified performance metrics to reliably measure the trajectory prediction performance. Through comprehensive experiments, we demonstrate the accuracy and robustness of the certified predictors and highlight their advantages over the non-certified ones. The code is available online: https://s-attack.github.io/ | [
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] |
35 | Evaluating Vision-Language Models as Evaluators in Path Planning | [
"Mohamed Aghzal",
"Xiang Yue",
"Erion Plaku",
"Ziyu Yao"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Aghzal_Evaluating_Vision-Language_Models_as_Evaluators_in_Path_Planning_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Aghzal_Evaluating_Vision-Language_Models_as_Evaluators_in_Path_Planning_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Aghzal_Evaluating_Vision-Language_Models_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2411.18711 | @InProceedings{Aghzal_2025_CVPR,
author = {Aghzal, Mohamed and Yue, Xiang and Plaku, Erion and Yao, Ziyu},
title = {Evaluating Vision-Language Models as Evaluators in Path Planning},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {6886-6897}
} | Despite their promise to perform complex reasoning, large language models (LLMs) have been shown to have limited effectiveness in end-to-end planning. This has inspired an intriguing question: if these models cannot plan well, can they still contribute to the planning framework as a helpful plan evaluator? In this work, we generalize this question to consider LLMs augmented with visual understanding, i.e., Vision-Language Models (VLMs). We introduce **PathEval**, a novel benchmark evaluating VLMs as plan evaluators in complex path-planning scenarios. Succeeding in the benchmark requires a VLM to be able to abstract traits of optimal paths from the scenario description, demonstrate precise low-level perception on each path, and integrate this information to decide the better path. Our analysis of state-of-the-art VLMs reveals that these models face significant challenges on the benchmark. We observe that the VLMs can precisely abstract given scenarios to identify the desired traits and exhibit mixed performance in integrating the provided information. Yet, their vision component presents a critical bottleneck, with models struggling to perceive low-level details about a path. Our experimental results show that this issue cannot be trivially addressed via end-to-end fine-tuning; rather, task-specific discriminative adaptation of these vision encoders is needed for these VLMs to become effective path evaluators. | [
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] |
36 | Free on the Fly: Enhancing Flexibility in Test-Time Adaptation with Online EM | [
"Qiyuan Dai",
"Sibei Yang"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Dai_Free_on_the_Fly_Enhancing_Flexibility_in_Test-Time_Adaptation_with_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Dai_Free_on_the_Fly_Enhancing_Flexibility_in_Test-Time_Adaptation_with_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Dai_Free_on_the_CVPR_2025_supplemental.pdf | null | @InProceedings{Dai_2025_CVPR,
author = {Dai, Qiyuan and Yang, Sibei},
title = {Free on the Fly: Enhancing Flexibility in Test-Time Adaptation with Online EM},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {9538-9548}
} | Vision-Language Models (VLMs) have become prominent in open-world image recognition for their strong generalization abilities. Yet, their effectiveness in practical applications is compromised by domain shifts and distributional changes, especially when test data distributions diverge from training data. Therefore, the paradigm of test-time adaptation (TTA) has emerged, enabling the use of online off-the-shelf data at test time, supporting independent sample predictions, and eliminating reliance on test annotations. Traditional TTA methods, however, often rely on costly training or optimization processes, or make unrealistic assumptions about accessing or storing historical training and test data.Instead, this study proposes FreeTTA, a training-free and universally available method that makes no assumptions, to enhance the flexibility of TTA. More importantly, FreeTTA is the first to explicitly model the test data distribution, enabling the use of intrinsic relationships among test samples to enhance predictions of individual samples without simultaneous access--a direction not previously explored. FreeTTA achieves these advantages by introducing an online EM algorithm that utilizes zero-shot predictions from VLMs as priors to iteratively compute the posterior probabilities of each online test sample and update parameters. Experiments demonstrate that FreeTTA achieves stable and significant improvements compared to state-of-the-art methods across 15 datasets in both cross-domain and out-of-distribution settings. | [
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] |
37 | Transformers without Normalization | [
"Jiachen Zhu",
"Xinlei Chen",
"Kaiming He",
"Yann LeCun",
"Zhuang Liu"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_Transformers_without_Normalization_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_Transformers_without_Normalization_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhu_Transformers_without_Normalization_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.10622 | @InProceedings{Zhu_2025_CVPR,
author = {Zhu, Jiachen and Chen, Xinlei and He, Kaiming and LeCun, Yann and Liu, Zhuang},
title = {Transformers without Normalization},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {14901-14911}
} | Normalization layers are ubiquitous in modern neural networks and have long been considered essential. This work demonstrates that Transformers without normalization can achieve the same or better performance using a remarkably simple technique. We introduce Dynamic Tanh (DyT), an element-wise operation DyT(x)=tanh(ax), as a drop-in replacement for normalization layers in Transformers. DyT is inspired by the observation that layer normalization in Transformers often produces tanh-like, S-shaped input-output mappings. By incorporating DyT, Transformers without normalization can match or exceed the performance of their normalized counterparts, mostly without hyperparameter tuning. We validate the effectiveness of Transformers with DyT across diverse settings, ranging from recognition to generation, supervised to self-supervised learning, and computer vision to language models. These findings challenge the conventional understanding that normalization layers are indispensable in modern neural networks, and offer new insights into their role in deep networks. | [
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38 | SGC-Net: Stratified Granular Comparison Network for Open-Vocabulary HOI Detection | [
"Xin Lin",
"Chong Shi",
"Zuopeng Yang",
"Haojin Tang",
"Zhili Zhou"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Lin_SGC-Net_Stratified_Granular_Comparison_Network_for_Open-Vocabulary_HOI_Detection_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Lin_SGC-Net_Stratified_Granular_Comparison_Network_for_Open-Vocabulary_HOI_Detection_CVPR_2025_paper.pdf | null | null | @InProceedings{Lin_2025_CVPR,
author = {Lin, Xin and Shi, Chong and Yang, Zuopeng and Tang, Haojin and Zhou, Zhili},
title = {SGC-Net: Stratified Granular Comparison Network for Open-Vocabulary HOI Detection},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {4539-4549}
} | Recent open-vocabulary human-object interaction (OV-HOI) detection methods primarily rely on large language model (LLM) for generating auxiliary descriptions and leverage knowledge distilled from CLIP to detect unseen interaction categories. Despite their effectiveness, these methods face two challenges: (1) feature granularity deficiency, due to reliance on last layer visual features for text alignment, leading to the neglect of crucial object-level details from intermediate layers; (2) semantic similarity confusion, resulting from CLIP's inherent biases toward certain classes, while LLM-generated descriptions based solely on labels fail to adequately capture inter-class similarities. To address these challenges, we propose a stratified granular comparison network. First, we introduce a granularity sensing alignment module that aggregates global semantic features with local details, refining interaction representations and ensuring robust alignment between intermediate visual features and text embeddings. Second, we develop a hierarchical group comparison module that recursively compares and groups classes using LLMs, generating fine-grained and discriminative descriptions for each interaction category. Experimental results on two widely-used benchmark datasets, SWIG-HOI and HICO-DET, demonstrate that our method achieves state-of-the-art results in OV-HOI detection. Codes is available at https://github.com/Phil0212/SGC-Net. | [
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39 | Galaxy Walker: Geometry-aware VLMs For Galaxy-scale Understanding | [
"Tianyu Chen",
"Xingcheng Fu",
"Yisen Gao",
"Haodong Qian",
"Yuecen Wei",
"Kun Yan",
"Haoyi Zhou",
"Jianxin Li"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Galaxy_Walker_Geometry-aware_VLMs_For_Galaxy-scale_Understanding_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Galaxy_Walker_Geometry-aware_VLMs_For_Galaxy-scale_Understanding_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Galaxy_Walker_Geometry-aware_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.18578 | @InProceedings{Chen_2025_CVPR,
author = {Chen, Tianyu and Fu, Xingcheng and Gao, Yisen and Qian, Haodong and Wei, Yuecen and Yan, Kun and Zhou, Haoyi and Li, Jianxin},
title = {Galaxy Walker: Geometry-aware VLMs For Galaxy-scale Understanding},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {4112-4121}
} | Modern vision-language models (VLMs) develop patch embedding and convolution backbone within vector space, especially Euclidean ones, at the very founding. When expanding VLMs to a galaxy-scale for understanding astronomical phenomena, the integration of spherical space for planetary orbits and hyperbolic spaces for black holes raises two formidable challenges. a) The current pre-training model is confined to Euclidean space rather than a comprehensive geometric embedding. b) The predominant architecture lacks suitable backbones for anisotropic physical geometries. In this paper, we introduced Galaxy-Walker, a geometry-aware VLM, for the universe-level vision understanding tasks. We proposed the geometry prompt that generates geometry tokens by random walks across diverse spaces on a multi-scale physical graph, along with a geometry adapter that compresses and reshapes the space anisotropy in a mixture-of-experts manner. Extensive experiments demonstrate the effectiveness of our approach, with Galaxy-Walker achieving state-of-the-art performance in both galaxy property estimation (R2 scores up to 0.91) and morphology classification tasks (up to +0.17 F1 improvement in challenging features), significantly outperforming both domain-specific models and general-purpose VLMs. | [
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40 | HiPART: Hierarchical Pose AutoRegressive Transformer for Occluded 3D Human Pose Estimation | [
"Hongwei Zheng",
"Han Li",
"Wenrui Dai",
"Ziyang Zheng",
"Chenglin Li",
"Junni Zou",
"Hongkai Xiong"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Zheng_HiPART_Hierarchical_Pose_AutoRegressive_Transformer_for_Occluded_3D_Human_Pose_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Zheng_HiPART_Hierarchical_Pose_AutoRegressive_Transformer_for_Occluded_3D_Human_Pose_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zheng_HiPART_Hierarchical_Pose_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.23331 | @InProceedings{Zheng_2025_CVPR,
author = {Zheng, Hongwei and Li, Han and Dai, Wenrui and Zheng, Ziyang and Li, Chenglin and Zou, Junni and Xiong, Hongkai},
title = {HiPART: Hierarchical Pose AutoRegressive Transformer for Occluded 3D Human Pose Estimation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {16807-16817}
} | Existing 2D-to-3D human pose estimation (HPE) methods struggle with the occlusion issue by enriching information like temporal and visual cues in the lifting stage. In this paper, we argue that these methods ignore the limitation of the sparse skeleton 2D input representation, which fundamentally restricts the 2D-to-3D lifting and worsens the occlusion issue. To address these, we propose a novel two-stage generative densification method, named Hierarchical Pose AutoRegressive Transformer (HiPART), to generate hierarchical 2D dense poses from the original sparse 2D pose. Specifically, we first develop a multi-scale skeleton tokenization module to quantize the highly dense 2D pose into hierarchical tokens and propose a skeleton-aware alignment to strengthen token connections. We then develop a hierarchical autoregressive modeling scheme for hierarchical 2D pose generation. With generated hierarchical poses as inputs for 2D-to-3D lifting, the proposed method shows strong robustness in occluded scenarios and achieves state-of-the-art performance on the single-frame-based 3D HPE. Moreover, it outperforms numerous multi-frame methods while reducing parameter and computational complexity and can also complement them to further enhance performance and robustness. | [
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41 | SnowMaster: Comprehensive Real-world Image Desnowing via MLLM with Multi-Model Feedback Optimization | [
"Jianyu Lai",
"Sixiang Chen",
"Yunlong Lin",
"Tian Ye",
"Yun Liu",
"Song Fei",
"Zhaohu Xing",
"Hongtao Wu",
"Weiming Wang",
"Lei Zhu"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Lai_SnowMaster_Comprehensive_Real-world_Image_Desnowing_via_MLLM_with_Multi-Model_Feedback_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Lai_SnowMaster_Comprehensive_Real-world_Image_Desnowing_via_MLLM_with_Multi-Model_Feedback_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lai_SnowMaster_Comprehensive_Real-world_CVPR_2025_supplemental.pdf | null | @InProceedings{Lai_2025_CVPR,
author = {Lai, Jianyu and Chen, Sixiang and Lin, Yunlong and Ye, Tian and Liu, Yun and Fei, Song and Xing, Zhaohu and Wu, Hongtao and Wang, Weiming and Zhu, Lei},
title = {SnowMaster: Comprehensive Real-world Image Desnowing via MLLM with Multi-Model Feedback Optimization},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {4302-4312}
} | Snowfall presents significant challenges for visual data processing, necessitating specialized desnowing algorithms. However, existing models often fail to generalize effectively due to their heavy reliance on synthetic datasets. Furthermore, current real-world snowfall datasets are limited in scale and lack dedicated evaluation metrics designed specifically for snowfall degradation, thus hindering the effective integration of real snowy images into model training to reduce domain gaps. To address these challenges, we first introduce RealSnow10K, a large-scale, high-quality dataset consisting of over 10,000 annotated real-world snowy images. In addition, we curate a preference dataset comprising 36,000 expert-ranked image pairs, enabling the adaptation of multimodal large language models (MLLMs) to better perceive snowy image quality through our innovative Multi-Model Preference Optimization (MMPO). Finally, we propose the SnowMaster, which employs MMPO-enhanced MLLM to perform accurate snowy image evaluation and pseudo-label filtering for semi-supervised training. Experiments demonstrate that SnowMaster delivers superior desnowing performance under real-world conditions. | [
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42 | From Faces to Voices: Learning Hierarchical Representations for High-quality Video-to-Speech | [
"Ji-Hoon Kim",
"Jeongsoo Choi",
"Jaehun Kim",
"Chaeyoung Jung",
"Joon Son Chung"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Kim_From_Faces_to_Voices_Learning_Hierarchical_Representations_for_High-quality_Video-to-Speech_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_From_Faces_to_Voices_Learning_Hierarchical_Representations_for_High-quality_Video-to-Speech_CVPR_2025_paper.pdf | null | http://arxiv.org/abs/2503.16956 | @InProceedings{Kim_2025_CVPR,
author = {Kim, Ji-Hoon and Choi, Jeongsoo and Kim, Jaehun and Jung, Chaeyoung and Chung, Joon Son},
title = {From Faces to Voices: Learning Hierarchical Representations for High-quality Video-to-Speech},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {15874-15884}
} | The objective of this study is to generate high-quality speech from silent talking face videos, a task also known as video-to-speech synthesis. A significant challenge in video-to-speech synthesis lies in the substantial modality gap between silent video and multi-faceted speech. In this paper, we propose a novel video-to-speech system that effectively bridges this modality gap, significantly enhancing the quality of synthesized speech. This is achieved by learning of hierarchical representations from video to speech. Specifically, we gradually transform silent video into acoustic feature spaces through three sequential stages -- content, timbre, and prosody modeling. In each stage, we align visual factors -- lip movements, face identity, and facial expressions -- with corresponding acoustic counterparts to ensure the seamless transformation. Additionally, to generate realistic and coherent speech from the visual representations, we employ a flow matching model that estimates direct trajectories from a simple prior distribution to the target speech distribution. Extensive experiments demonstrate that our method achieves exceptional generation quality comparable to real utterances, outperforming existing methods by a significant margin. | [
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] |
43 | DFM: Differentiable Feature Matching for Anomaly Detection | [
"Sheng Wu",
"Yimi Wang",
"Xudong Liu",
"Yuguang Yang",
"Runqi Wang",
"Guodong Guo",
"David Doermann",
"Baochang Zhang"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Wu_DFM_Differentiable_Feature_Matching_for_Anomaly_Detection_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_DFM_Differentiable_Feature_Matching_for_Anomaly_Detection_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_DFM_Differentiable_Feature_CVPR_2025_supplemental.pdf | null | @InProceedings{Wu_2025_CVPR,
author = {Wu, Sheng and Wang, Yimi and Liu, Xudong and Yang, Yuguang and Wang, Runqi and Guo, Guodong and Doermann, David and Zhang, Baochang},
title = {DFM: Differentiable Feature Matching for Anomaly Detection},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {15224-15233}
} | Feature matching methods for unsupervised anomaly detection have demonstrated impressive performance. Existing methods primarily rely on self-supervised training and handcrafted matching schemes for task adaptation. However, they can only achieve an inferior feature representation for anomaly detection because the feature extraction and matching modules are separately trained. To address these issues, we propose a Differentiable Feature Matching (DFM) framework for joint optimization of the feature extractor and the matching head. DFM transforms nearest-neighbor matching into a pooling-based module and embeds it within a Feature Matching Network (FMN). This design enables end-to-end feature extraction and feature matching module training, thus providing better feature representation for anomaly detection tasks. DFM is generic and can be incorporated into existing feature-matching methods. We implement DFM with various backbones and conduct extensive experiments across various tasks and datasets, demonstrating its effectiveness. Notably, we achieve state-of-the-art results in the continual anomaly detection task with instance-AUROC improvement of up to 3.9% and pixel-AP improvement of up to 5.5%. | [
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] |
44 | FlashGS: Efficient 3D Gaussian Splatting for Large-scale and High-resolution Rendering | [
"Guofeng Feng",
"Siyan Chen",
"Rong Fu",
"Zimu Liao",
"Yi Wang",
"Tao Liu",
"Boni Hu",
"Linning Xu",
"Zhilin Pei",
"Hengjie Li",
"Xiuhong Li",
"Ninghui Sun",
"Xingcheng Zhang",
"Bo Dai"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Feng_FlashGS_Efficient_3D_Gaussian_Splatting_for_Large-scale_and_High-resolution_Rendering_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Feng_FlashGS_Efficient_3D_Gaussian_Splatting_for_Large-scale_and_High-resolution_Rendering_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Feng_FlashGS_Efficient_3D_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2408.07967 | @InProceedings{Feng_2025_CVPR,
author = {Feng, Guofeng and Chen, Siyan and Fu, Rong and Liao, Zimu and Wang, Yi and Liu, Tao and Hu, Boni and Xu, Linning and Pei, Zhilin and Li, Hengjie and Li, Xiuhong and Sun, Ninghui and Zhang, Xingcheng and Dai, Bo},
title = {FlashGS: Efficient 3D Gaussian Splatting for Large-scale and High-resolution Rendering},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {26652-26662}
} | Recent advances in 3D Gaussian Splatting (3DGS) have demonstrated significant potential over traditional rendering techniques, attracting widespread attention from both industry and academia. However, real-time rendering with 3DGS remains a challenging problem, particularly in large-scale, high-resolution scenes due to the presence of numerous anisotropic Gaussian representations, and it has not been extensively explored. To address this challenge, we introduce FlashGS, an open-source CUDA library with Python bindings, featuring comprehensive algorithm design and optimizations, including redundancy elimination, adaptive scheduling, and efficient pipelining. First, we eliminate substantial redundant computations through precise Gaussian intersection tests, leveraging the intrinsic mechanism of the 3DGS rasterizer. During task partitioning, we propose an adaptive scheduling strategy that accounts for variations in Gaussian size and shape. Additionally, we design a multi-stage pipelining strategy for color computation in the rendering process, further accelerating performance. We conduct an extensive evaluation of FlashGS across a diverse range of synthetic and real-world 3D scenes, encompassing scene sizes of up to 2.7 km^2 cityscape and resolutions of up to over 4K. Our approach improves 3DGS rendering performance by an order of magnitude, achieving an average speedup of 7.2x, and rendering at a minimum of 125.9 FPS, setting a new state-of-the-art in real-time 3DGS rendering. https://github.com/InternLandMark/FlashGS. | [
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] |
45 | PointSR: Self-Regularized Point Supervision for Drone-View Object Detection | [
"Weizhuo Li",
"Yue Xi",
"Wenjing Jia",
"Zehao Zhang",
"Fei Li",
"Xiangzeng Liu",
"Qiguang Miao"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Li_PointSR_Self-Regularized_Point_Supervision_for_Drone-View_Object_Detection_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Li_PointSR_Self-Regularized_Point_Supervision_for_Drone-View_Object_Detection_CVPR_2025_paper.pdf | null | null | @InProceedings{Li_2025_CVPR,
author = {Li, Weizhuo and Xi, Yue and Jia, Wenjing and Zhang, Zehao and Li, Fei and Liu, Xiangzeng and Miao, Qiguang},
title = {PointSR: Self-Regularized Point Supervision for Drone-View Object Detection},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {11707-11716}
} | Point-Supervised Object Detection (PSOD) in a discriminative style has recently gained significant attention for its impressive detection performance and cost-effectiveness. However, accurately predicting high-quality pseudo-box labels for drone-view images, which often feature densely packed small objects, remains a challenge. This difficulty arises primarily from the limitation of rigid sampling strategies, which hinder the pseudo-box optimization process. To address this, we propose PointSR, an effective and robust point-supervised object detection framework with self-regularized sampling that integrates temporal and informative constraints throughout the pseudo-box generation process. Specifically, the framework comprises three key components: Temporal-Ensembling Encoder (TE Encoder), Coarse Pseudo-box Prediction, and Pseudo-box Refinement. The TE Encoder builds an anchor prototype library by aggregating temporal information for dynamic anchor adjustment. In Coarse Pseudo-box Prediction, anchors are refined using the prototype library, and a set of informative samples is collected for subsequent refinement. During Pseudo-box Refinement, these informative negative samples are used to suppress low-confidence candidate positive samples, thereby improving the quality of the pseudo-boxes. Experimental results on benchmark datasets demonstrate that PointSR significantly outperforms state-of-the-art methods, achieving up to 2.6%~\mathbf 7.2% higher AP_ 50 using only point supervision. Additionally, it exhibits strong robustness to perturbation in human-labeled points. | [
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] |
46 | Exploring Timeline Control for Facial Motion Generation | [
"Yifeng Ma",
"Jinwei Qi",
"Chaonan Ji",
"Peng Zhang",
"Bang Zhang",
"Zhidong Deng",
"Liefeng Bo"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Ma_Exploring_Timeline_Control_for_Facial_Motion_Generation_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Ma_Exploring_Timeline_Control_for_Facial_Motion_Generation_CVPR_2025_paper.pdf | null | http://arxiv.org/abs/2505.20861 | @InProceedings{Ma_2025_CVPR,
author = {Ma, Yifeng and Qi, Jinwei and Ji, Chaonan and Zhang, Peng and Zhang, Bang and Deng, Zhidong and Bo, Liefeng},
title = {Exploring Timeline Control for Facial Motion Generation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {1940-1950}
} | This paper introduces a new control signal for facial motion generation: timeline control. Compared to audio and text signals, timelines provide more fine-grained control, such as generating specific facial motions with precise timing. Users can specify a multi-track timeline of facial actions arranged in temporal intervals, allowing precise control over the timing of each action. To model the timeline control capability, We first annotate the time intervals of facial actions in natural facial motion sequences at a frame-level granularity. This process is facilitated by Toeplitz Inverse Covariance-based Clustering to minimize human labor. Based on the annotations, we propose a diffusion-based generation model capable of generating facial motions that are natural and accurately aligned with input timelines. Our method supports text-guided motion generation by using ChatGPT to convert text into timelines. Experimental results show that our method can annotate facial action intervals with satisfactory accuracy, and produces natural facial motions accurately aligned with timelines. | [
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] |
47 | v-CLR: View-Consistent Learning for Open-World Instance Segmentation | [
"Chang-Bin Zhang",
"Jinhong Ni",
"Yujie Zhong",
"Kai Han"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_v-CLR_View-Consistent_Learning_for_Open-World_Instance_Segmentation_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_v-CLR_View-Consistent_Learning_for_Open-World_Instance_Segmentation_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_v-CLR_View-Consistent_Learning_CVPR_2025_supplemental.pdf | null | @InProceedings{Zhang_2025_CVPR,
author = {Zhang, Chang-Bin and Ni, Jinhong and Zhong, Yujie and Han, Kai},
title = {v-CLR: View-Consistent Learning for Open-World Instance Segmentation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {20307-20317}
} | In this paper, we address the challenging problem of open-world instance segmentation. Existing works have shown that vanilla visual networks are biased toward learning appearance information, e.g. texture, to recognize objects. This implicit bias causes the model to fail in detecting novel objects with unseen textures in the open-world setting. To address this challenge, we propose a learning framework, called view-Consistent LeaRning (v-CLR), which aims to enforce the model to learn appearance-invariant representations for robust instance segmentation. In v-CLR, we first introduce additional views for each image, where the texture undergoes significant alterations while preserving the image's underlying structure. We then encourage the model to learn the appearance-invariant representation by enforcing the consistency between object features across different views, for which we obtain class-agnostic object proposals using off-the-shelf unsupervised models that possess strong object-awareness. These proposals enable cross-view object feature matching, greatly reducing the appearance dependency while enhancing the object-awareness. We thoroughly evaluate our method on public benchmarks under both cross-class and cross-dataset settings, achieving state-of-the-art performance. Project page: https://visual-ai.github.io/vclr | [
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48 | Chat2SVG: Vector Graphics Generation with Large Language Models and Image Diffusion Models | [
"Ronghuan Wu",
"Wanchao Su",
"Jing Liao"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Wu_Chat2SVG_Vector_Graphics_Generation_with_Large_Language_Models_and_Image_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_Chat2SVG_Vector_Graphics_Generation_with_Large_Language_Models_and_Image_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_Chat2SVG_Vector_Graphics_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2411.16602 | @InProceedings{Wu_2025_CVPR,
author = {Wu, Ronghuan and Su, Wanchao and Liao, Jing},
title = {Chat2SVG: Vector Graphics Generation with Large Language Models and Image Diffusion Models},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {23690-23700}
} | Scalable Vector Graphics (SVG) has become the de facto standard for vector graphics in digital design, offering resolution independence and precise control over individual elements. Despite their advantages, creating high-quality SVG content remains challenging, as it demands technical expertise with professional editing software and a considerable time investment to craft complex shapes. Recent text-to-SVG generation methods aim to make vector graphics creation more accessible, but they still encounter limitations in shape regularity, generalization ability, and expressiveness. To address these challenges, we introduce Chat2SVG, a hybrid framework that combines the strengths of Large Language Models (LLMs) and image diffusion models for text-to-SVG generation. Our approach first uses an LLM to generate semantically meaningful SVG templates from basic geometric primitives. Guided by image diffusion models, a dual-stage optimization pipeline refines paths in latent space and adjusts point coordinates to enhance geometric complexity. Extensive experiments show that Chat2SVG outperforms existing methods in visual fidelity, path regularity, and semantic alignment. Additionally, our system enables intuitive editing through natural language instructions, making professional vector graphics creation accessible to all users. Our code is available at https://chat2svg.github.io/. | [
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49 | GAF: Gaussian Avatar Reconstruction from Monocular Videos via Multi-view Diffusion | [
"Jiapeng Tang",
"Davide Davoli",
"Tobias Kirschstein",
"Liam Schoneveld",
"Matthias Nießner"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Tang_GAF_Gaussian_Avatar_Reconstruction_from_Monocular_Videos_via_Multi-view_Diffusion_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Tang_GAF_Gaussian_Avatar_Reconstruction_from_Monocular_Videos_via_Multi-view_Diffusion_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tang_GAF_Gaussian_Avatar_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2412.10209 | @InProceedings{Tang_2025_CVPR,
author = {Tang, Jiapeng and Davoli, Davide and Kirschstein, Tobias and Schoneveld, Liam and Nie{\ss}ner, Matthias},
title = {GAF: Gaussian Avatar Reconstruction from Monocular Videos via Multi-view Diffusion},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {5546-5558}
} | We propose a novel approach for reconstructing animatable 3D Gaussian avatars from monocular videos captured by commodity devices like smartphones. Photorealistic 3D head avatar reconstruction from such recordings is challenging due to limited observations, which leaves unobserved regions under-constrained and can lead to artifacts in novel views. To address this problem, we introduce a multi-view head diffusion model, leveraging its priors to fill in missing regions and ensure view consistency in Gaussian splatting renderings. To enable precise viewpoint control, we use normal maps rendered from FLAME-based head reconstruction, which provides pixel-aligned inductive biases. We also condition the diffusion model on VAE features extracted from the input image to preserve facial identity and appearance details. For Gaussian avatar reconstruction, we distill multi-view diffusion priors by using iteratively denoised images as pseudo-ground truths, effectively mitigating over-saturation issues. To further improve photorealism, we apply latent upsampling priors to refine the denoised latent before decoding it into an image. We evaluate our method on the NeRSemble dataset, showing that GAF outperforms previous state-of-the-art methods in novel view synthesis. Furthermore, we demonstrate higher-fidelity avatar reconstructions from monocular videos captured on commodity devices. | [
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50 | Reloc3r: Large-Scale Training of Relative Camera Pose Regression for Generalizable, Fast, and Accurate Visual Localization | [
"Siyan Dong",
"Shuzhe Wang",
"Shaohui Liu",
"Lulu Cai",
"Qingnan Fan",
"Juho Kannala",
"Yanchao Yang"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Dong_Reloc3r_Large-Scale_Training_of_Relative_Camera_Pose_Regression_for_Generalizable_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Dong_Reloc3r_Large-Scale_Training_of_Relative_Camera_Pose_Regression_for_Generalizable_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Dong_Reloc3r_Large-Scale_Training_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2412.08376 | @InProceedings{Dong_2025_CVPR,
author = {Dong, Siyan and Wang, Shuzhe and Liu, Shaohui and Cai, Lulu and Fan, Qingnan and Kannala, Juho and Yang, Yanchao},
title = {Reloc3r: Large-Scale Training of Relative Camera Pose Regression for Generalizable, Fast, and Accurate Visual Localization},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {16739-16752}
} | Visual localization aims to determine the camera pose of a query image relative to a database of posed images. In recent years, deep neural networks that directly regress camera poses have gained popularity due to their fast inference capabilities. However, existing methods struggle to either generalize well to new scenes or provide accurate camera pose estimates. To address these issues, we present Reloc3r, a simple yet effective visual localization framework. It consists of an elegantly designed relative pose regression network, and a minimalist motion averaging module for absolute pose estimation. Trained on approximately eight million posed image pairs, Reloc3r achieves surprisingly good performance and generalization ability. We conduct extensive experiments on six public datasets, consistently demonstrating the effectiveness and efficiency of the proposed method. It provides high-quality camera pose estimates in real time and generalizes to novel scenes. Code: https://github.com/ffrivera0/reloc3r. | [
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] |
51 | AI-Face: A Million-Scale Demographically Annotated AI-Generated Face Dataset and Fairness Benchmark | [
"Li Lin",
"Santosh Santosh",
"Mingyang Wu",
"Xin Wang",
"Shu Hu"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Lin_AI-Face_A_Million-Scale_Demographically_Annotated_AI-Generated_Face_Dataset_and_Fairness_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Lin_AI-Face_A_Million-Scale_Demographically_Annotated_AI-Generated_Face_Dataset_and_Fairness_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lin_AI-Face_A_Million-Scale_CVPR_2025_supplemental.pdf | null | @InProceedings{Lin_2025_CVPR,
author = {Lin, Li and Santosh, Santosh and Wu, Mingyang and Wang, Xin and Hu, Shu},
title = {AI-Face: A Million-Scale Demographically Annotated AI-Generated Face Dataset and Fairness Benchmark},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {3503-3515}
} | AI-generated faces have enriched human life, such as entertainment, education, and art. However, they also pose misuse risks. Therefore, detecting AI-generated faces becomes crucial, yet current detectors show biased performance across different demographic groups. Mitigating biases can be done by designing algorithmic fairness methods, which usually require demographically annotated face datasets for model training. However, no existing dataset encompasses both demographic attributes and diverse generative methods simultaneously, which hinders the development of fair detectors for AI-generated faces. In this work, we introduce the AI-Face dataset, the first million-scale demographically annotated AI-generated face image dataset, including real faces, faces from deepfake videos, and faces generated by Generative Adversarial Networks and Diffusion Models. Based on this dataset, we conduct the first comprehensive fairness benchmark to assess various AI face detectors and provide valuable insights and findings to promote the future fair design of AI face detectors. Our AI-Face dataset and benchmark code are publicly available at https://github.com/Purdue-M2/AI-Face-FairnessBench. | [
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] |
52 | Inference-Scale Complexity in ANN-SNN Conversion for High-Performance and Low-Power Applications | [
"Tong Bu",
"Maohua Li",
"Zhaofei Yu"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Bu_Inference-Scale_Complexity_in_ANN-SNN_Conversion_for_High-Performance_and_Low-Power_Applications_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Bu_Inference-Scale_Complexity_in_ANN-SNN_Conversion_for_High-Performance_and_Low-Power_Applications_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bu_Inference-Scale_Complexity_in_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2409.03368 | @InProceedings{Bu_2025_CVPR,
author = {Bu, Tong and Li, Maohua and Yu, Zhaofei},
title = {Inference-Scale Complexity in ANN-SNN Conversion for High-Performance and Low-Power Applications},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {24387-24397}
} | Spiking Neural Networks (SNNs) have emerged as a promising substitute for Artificial Neural Networks (ANNs) due to their advantages of fast inference and low power consumption. However, the lack of efficient training algorithms has hindered their widespread adoption. Even efficient ANN-SNN conversion methods necessitate quantized training of ANNs to enhance the effectiveness of the conversion, incurring additional training costs. To address these challenges, we propose an efficient ANN-SNN conversion framework with only inference scale complexity. The conversion framework includes a local threshold balancing algorithm, which enables efficient calculation of the optimal thresholds and fine-grained adjustment of the threshold value by channel-wise scaling. We also introduce an effective delayed evaluation strategy to mitigate the influence of the spike propagation delays. We demonstrate the scalability of our framework in typical computer vision tasks: image classification, semantic segmentation, object detection, and video classification. Our algorithm outperforms existing methods, highlighting its practical applicability and efficiency. Moreover, we have evaluated the energy consumption of the converted SNNs, demonstrating their superior low-power advantage compared to conventional ANNs. This approach simplifies the deployment of SNNs by leveraging open-source pre-trained ANN models, enabling fast, low-power inference with negligible performance reduction. | [
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] |
53 | Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation | [
"Chengyue Wu",
"Xiaokang Chen",
"Zhiyu Wu",
"Yiyang Ma",
"Xingchao Liu",
"Zizheng Pan",
"Wen Liu",
"Zhenda Xie",
"Xingkai Yu",
"Chong Ruan",
"Ping Luo"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Wu_Janus_Decoupling_Visual_Encoding_for_Unified_Multimodal_Understanding_and_Generation_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_Janus_Decoupling_Visual_Encoding_for_Unified_Multimodal_Understanding_and_Generation_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_Janus_Decoupling_Visual_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2410.13848 | @InProceedings{Wu_2025_CVPR,
author = {Wu, Chengyue and Chen, Xiaokang and Wu, Zhiyu and Ma, Yiyang and Liu, Xingchao and Pan, Zizheng and Liu, Wen and Xie, Zhenda and Yu, Xingkai and Ruan, Chong and Luo, Ping},
title = {Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {12966-12977}
} | We introduce Janus, an autoregressive framework that unifies multimodal understanding and generation. Prior research often relies on a single visual encoder for both tasks, such as Chameleon. However, due to the differing levels of information granularity required by multimodal understanding and generation, this approach can lead to suboptimal performance, particularly in multimodal understanding. To address this issue, we decouple visual encoding into separate pathways, while still leveraging a single, unified transformer architecture for processing. The decoupling not only alleviates the conflict between the visual encoder's roles in understanding and generation, but also enhances the framework's flexibility. For instance, both the multimodal understanding and generation components can independently select their most suitable encoding methods. Experiments show that Janus surpasses previous unified model and matches or exceeds the performance of task-specific models. The simplicity, high flexibility, and effectiveness of Janus make it a strong candidate for next-generation unified multimodal models. | [
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54 | MVDoppler-Pose: Multi-Modal Multi-View mmWave Sensing for Long-Distance Self-Occluded Human Walking Pose Estimation | [
"Jaeho Choi",
"Soheil Hor",
"Shubo Yang",
"Amin Arbabian"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Choi_MVDoppler-Pose_Multi-Modal_Multi-View_mmWave_Sensing_for_Long-Distance_Self-Occluded_Human_Walking_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Choi_MVDoppler-Pose_Multi-Modal_Multi-View_mmWave_Sensing_for_Long-Distance_Self-Occluded_Human_Walking_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Choi_MVDoppler-Pose_Multi-Modal_Multi-View_CVPR_2025_supplemental.pdf | null | @InProceedings{Choi_2025_CVPR,
author = {Choi, Jaeho and Hor, Soheil and Yang, Shubo and Arbabian, Amin},
title = {MVDoppler-Pose: Multi-Modal Multi-View mmWave Sensing for Long-Distance Self-Occluded Human Walking Pose Estimation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {27750-27759}
} | One of the main challenges in reliable camera-based 3D pose estimation for walking subjects is to deal with self-occlusions, especially in the case of using low-resolution cameras or at longer distance scenarios. In recent years, millimeter-wave (mmWave) radar has emerged as a promising alternative, offering inherent resilience to the effect of occlusions and distance variations. However, mmWave-based human walking pose estimation (HWPE) is still in the nascent development stages, primarily due to its unique set of practical challenges including the quality of the observed radar signal dependent on the subject's motion direction. This paper introduces the first comprehensive study comparing mmWave radar to camera systems for HWPE, highlighting its utility for distance-agnostic and occlusion-resilient pose estimation. Building upon mmWave's unique advantages, we address its intrinsic directionality issue through a new approach--the synergetic integration of multi-modal, multi-view mmWave signals, achieving robust HWPE against variations both in distance and walking direction. Extensive experiments on a newly curated dataset not only demonstrate the superior potential of mmWave technology over traditional camera-based HWPE systems, but also validate the effectiveness of our approach in overcoming the core limitations of mmWave HWPE. | [
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55 | TopNet: Transformer-Efficient Occupancy Prediction Network for Octree-Structured Point Cloud Geometry Compression | [
"Xinjie Wang",
"Yifan Zhang",
"Ting Liu",
"Xinpu Liu",
"Ke Xu",
"Jianwei Wan",
"Yulan Guo",
"Hanyun Wang"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Wang_TopNet_Transformer-Efficient_Occupancy_Prediction_Network_for_Octree-Structured_Point_Cloud_Geometry_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_TopNet_Transformer-Efficient_Occupancy_Prediction_Network_for_Octree-Structured_Point_Cloud_Geometry_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_TopNet_Transformer-Efficient_Occupancy_CVPR_2025_supplemental.pdf | null | @InProceedings{Wang_2025_CVPR,
author = {Wang, Xinjie and Zhang, Yifan and Liu, Ting and Liu, Xinpu and Xu, Ke and Wan, Jianwei and Guo, Yulan and Wang, Hanyun},
title = {TopNet: Transformer-Efficient Occupancy Prediction Network for Octree-Structured Point Cloud Geometry Compression},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {27305-27314}
} | Efficient Point Cloud Geometry Compression (PCGC) with a lower bits per point (BPP) and higher peak signal-to-noise ratio (PSNR) is essential for the transportation of large-scale 3D data. Although octree-based entropy models can reduce BPP without introducing geometry distortion, existing CNN-based models struggle with limited receptive fields to capture long-range dependencies, while Transformer-built architectures always neglect fine-grained details due to their reliance on global self-attention. In this paper, we propose a Transformer-efficient occupancy prediction Network, termed TopNet, to overcome these challenges by developing several novel components: Locally-enhanced Context Encoding (LeCE) for enhancing the translation-invariance of the octree nodes, Adaptive-Length Sliding Window Attention (AL-SWA) for capturing both global and local dependencies while adaptively adjusting attention weights based on the input window length, Spatial-Gated-enhanced Channel Mixer (SG-CM) for efficient feature aggregation from ancestors and siblings, and Latent-guided Node Occupancy Predictor (LNOP) for improving prediction accuracy of spatially adjacent octree nodes. Comprehensive experiments across both indoor and outdoor point cloud datasets demonstrate that our TopNet achieves state-of-the-art performance with fewer parameters, further advancing the reduction-efficiency boundaries of PCGC. The code is available at https://github.com/xinjiewang1995/TopNet. | [
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56 | MagicArticulate: Make Your 3D Models Articulation-Ready | [
"Chaoyue Song",
"Jianfeng Zhang",
"Xiu Li",
"Fan Yang",
"Yiwen Chen",
"Zhongcong Xu",
"Jun Hao Liew",
"Xiaoyang Guo",
"Fayao Liu",
"Jiashi Feng",
"Guosheng Lin"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Song_MagicArticulate_Make_Your_3D_Models_Articulation-Ready_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Song_MagicArticulate_Make_Your_3D_Models_Articulation-Ready_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Song_MagicArticulate_Make_Your_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2502.12135 | @InProceedings{Song_2025_CVPR,
author = {Song, Chaoyue and Zhang, Jianfeng and Li, Xiu and Yang, Fan and Chen, Yiwen and Xu, Zhongcong and Liew, Jun Hao and Guo, Xiaoyang and Liu, Fayao and Feng, Jiashi and Lin, Guosheng},
title = {MagicArticulate: Make Your 3D Models Articulation-Ready},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {15998-16007}
} | With the explosive growth of 3D content creation, there is an increasing demand for automatically converting static 3D models into articulation-ready versions that support realistic animation. Traditional approaches rely heavily on manual annotation, which is both time-consuming and labor-intensive. Moreover, the lack of large-scale benchmarks has hindered the development of learning-based solutions. In this work, we present MagicArticulate, an effective framework that automatically transforms static 3D models into articulation-ready assets. Our key contributions are threefold. First, we introduce Articulation-XL, a large-scale benchmark containing over 33k 3D models with high-quality articulation annotations, carefully curated from Objaverse-XL. Second, we propose a novel skeleton generation method that formulates the task as a sequence modeling problem, leveraging an auto-regressive transformer to naturally handle varying numbers of bones or joints within skeletons and their inherent dependencies across different 3D models. Third, we predict skinning weights using a functional diffusion process that incorporates volumetric geodesic distance priors between vertices and joints. Extensive experiments demonstrate that MagicArticulate significantly outperforms existing methods across diverse object categories, achieving high-quality articulation that enables realistic animation. Project page: https://chaoyuesong.github.io/MagicArticulate. | [
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57 | Gain from Neighbors: Boosting Model Robustness in the Wild via Adversarial Perturbations Toward Neighboring Classes | [
"Zhou Yang",
"Mingtao Feng",
"Tao Huang",
"Fangfang Wu",
"Weisheng Dong",
"Xin Li",
"Guangming Shi"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Yang_Gain_from_Neighbors_Boosting_Model_Robustness_in_the_Wild_via_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_Gain_from_Neighbors_Boosting_Model_Robustness_in_the_Wild_via_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_Gain_from_Neighbors_CVPR_2025_supplemental.pdf | null | @InProceedings{Yang_2025_CVPR,
author = {Yang, Zhou and Feng, Mingtao and Huang, Tao and Wu, Fangfang and Dong, Weisheng and Li, Xin and Shi, Guangming},
title = {Gain from Neighbors: Boosting Model Robustness in the Wild via Adversarial Perturbations Toward Neighboring Classes},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {25497-25507}
} | Recent approaches, such as data augmentation, adversarial training, and transfer learning, have shown potential in addressing the issue of performance degradation caused by distributional shifts. However, they typically demand careful design in terms of data or models and lack awareness of the impact of distributional shifts. In this paper, we observe that classification errors arising from distribution shifts tend to cluster near the true values, suggesting that misclassifications commonly occur in semantically similar, neighboring categories. Furthermore, robust advanced vision foundation models maintain larger inter-class distances while preserving semantic consistency, making them less vulnerable to such shifts. Building on these findings, we propose a new method called GFN (Gain From Neighbors), which uses gradient priors from neighboring classes to perturb input images and incorporates an inter-class distance-weighted loss to improve class separation. This approach encourages the model to learn more resilient features from data prone to errors, enhancing its robustness against shifts in diverse settings. In extensive experiments across various model architectures and benchmark datasets, GFN consistently demonstrated superior performance. For instance, compared to the current state-of-the-art TAPADL method, our approach achieved a higher corruption robustness of 41.4% on ImageNet-C (+2.3%), without requiring additional parameters and using only minimal data. | [
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58 | Enhancing Video-LLM Reasoning via Agent-of-Thoughts Distillation | [
"Yudi Shi",
"Shangzhe Di",
"Qirui Chen",
"Weidi Xie"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Shi_Enhancing_Video-LLM_Reasoning_via_Agent-of-Thoughts_Distillation_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Shi_Enhancing_Video-LLM_Reasoning_via_Agent-of-Thoughts_Distillation_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shi_Enhancing_Video-LLM_Reasoning_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2412.01694 | @InProceedings{Shi_2025_CVPR,
author = {Shi, Yudi and Di, Shangzhe and Chen, Qirui and Xie, Weidi},
title = {Enhancing Video-LLM Reasoning via Agent-of-Thoughts Distillation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {8523-8533}
} | This paper tackles the problem of video question answering (VideoQA), a task that often requires multi-step reasoning and a profound understanding of spatial-temporal dynamics. While large video-language models perform well on benchmarks, they often lack explainability and spatial-temporal grounding. In this paper, we propose **A**gent-**o**f-**T**houghts **D**istillation (**AoTD**), a method that enhances models by incorporating automatically generated Chain-of-Thoughts (CoTs) into the instruction-tuning process. Specifically, we leverage an agent-based system to decompose complex questions into sub-tasks, and address them with specialized vision models, the intermediate results are then treated as reasoning chains. We also introduce a verification mechanism using a large language model (LLM) to ensure the reliability of generated CoTs. Extensive experiments demonstrate that AoTD improves the performance on multiple-choice and open-ended benchmarks. | [
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59 | De^2Gaze: Deformable and Decoupled Representation Learning for 3D Gaze Estimation | [
"Yunfeng Xiao",
"Xiaowei Bai",
"Baojun Chen",
"Hao Su",
"Hao He",
"Liang Xie",
"Erwei Yin"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Xiao_De2Gaze_Deformable_and_Decoupled_Representation_Learning_for_3D_Gaze_Estimation_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Xiao_De2Gaze_Deformable_and_Decoupled_Representation_Learning_for_3D_Gaze_Estimation_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xiao_De2Gaze_Deformable_and_CVPR_2025_supplemental.pdf | null | @InProceedings{Xiao_2025_CVPR,
author = {Xiao, Yunfeng and Bai, Xiaowei and Chen, Baojun and Su, Hao and He, Hao and Xie, Liang and Yin, Erwei},
title = {De{\textasciicircum}2Gaze: Deformable and Decoupled Representation Learning for 3D Gaze Estimation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {3091-3100}
} | 3D Gaze estimation is a challenging task due to two main issues. First, existing methods focus on analyzing dense features (e.g., large pixel regions), which are sensitive to local noise (e.g., light spots, blurs) and result in increased computational complexity. Second, an eyeball model can correspond multiple gaze directions, and the entangled representation between gazes and models increases the learning difficulty. To address these issues, we propose De\textsuperscript 2Gaze , a lightweight and accurate model-aware 3D gaze estimation method. In De\textsuperscript 2 Gaze, we introduce two key innovations for deformable and decoupled representation learning. Specifically, first, we propose a deformable sparse attention mechanism that can adapt sparse sampling points to attention areas to avoid local noise influences. Second, we propose a spatial decoupling network with a dual-branch decoding architecture to disentangle invariant (e.g., eyeball radius, position) and variable (e.g., gaze, pupil, iris) features from the latent space. Compared to existing methods, De\textsuperscript 2 Gaze requires fewer sparse features, and achieves faster convergence speed, lower computational complexity, and higher accuracy in 3D gaze estimation.Qualitative and quantitative experiments demonstrate that De\textsuperscript 2 Gaze achieves state-of-the-art accuracy and high-quality semantic segmentation for 3D gaze estimation on the TEyeD dataset. | [
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60 | ReCapture: Generative Video Camera Controls for User-Provided Videos using Masked Video Fine-Tuning | [
"David Junhao Zhang",
"Roni Paiss",
"Shiran Zada",
"Nikhil Karnad",
"David E. Jacobs",
"Yael Pritch",
"Inbar Mosseri",
"Mike Zheng Shou",
"Neal Wadhwa",
"Nataniel Ruiz"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_ReCapture_Generative_Video_Camera_Controls_for_User-Provided_Videos_using_Masked_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_ReCapture_Generative_Video_Camera_Controls_for_User-Provided_Videos_using_Masked_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_ReCapture_Generative_Video_CVPR_2025_supplemental.zip | http://arxiv.org/abs/2411.05003 | @InProceedings{Zhang_2025_CVPR,
author = {Zhang, David Junhao and Paiss, Roni and Zada, Shiran and Karnad, Nikhil and Jacobs, David E. and Pritch, Yael and Mosseri, Inbar and Shou, Mike Zheng and Wadhwa, Neal and Ruiz, Nataniel},
title = {ReCapture: Generative Video Camera Controls for User-Provided Videos using Masked Video Fine-Tuning},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {2050-2062}
} | Recently, breakthroughs in video modeling have allowed for controllable camera trajectories in generated videos. However, these methods cannot be directly applied to user-provided videos that are not generated by a video model. In this paper, we present ReCapture, a method for generating new videos with novel camera trajectories from a single user-provided video. Our method allows us to re-generate the reference video, with all its existing scene motion, from vastly different angles and with cinematic camera motion. Notably, using our method we can also plausibly hallucinate parts of the scene that were not observable in the reference video. Our method works by (1) generating a noisy anchor video with a new camera trajectory using multiview diffusion models or depth-based point cloud rendering and then (2) regenerating the anchor video into a clean and temporally consistent reangled video using our proposed masked video fine-tuning technique. | [
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] |
61 | M^3-VOS: Multi-Phase, Multi-Transition, and Multi-Scenery Video Object Segmentation | [
"Zixuan Chen",
"Jiaxin Li",
"Junxuan Liang",
"Liming Tan",
"Yejie Guo",
"Cewu Lu",
"Yong-Lu Li"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Chen_M3-VOS_Multi-Phase_Multi-Transition_and_Multi-Scenery_Video_Object_Segmentation_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_M3-VOS_Multi-Phase_Multi-Transition_and_Multi-Scenery_Video_Object_Segmentation_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_M3-VOS_Multi-Phase_Multi-Transition_CVPR_2025_supplemental.zip | null | @InProceedings{Chen_2025_CVPR,
author = {Chen, Zixuan and Li, Jiaxin and Liang, Junxuan and Tan, Liming and Guo, Yejie and Lu, Cewu and Li, Yong-Lu},
title = {M{\textasciicircum}3-VOS: Multi-Phase, Multi-Transition, and Multi-Scenery Video Object Segmentation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {29193-29202}
} | Intelligent robots need to interact with diverse objects across various environments. The appearance and state of objects frequently undergo complex transformations depending on the object properties, e.g., phase transitions. However, in the vision community, segmenting dynamic objects with phase transitions is overlooked. In light of this, we introduce the concept of phase in segmentation, which categorizes real-world objects based on their visual characteristics and potential morphological and appearance changes. Then, we present a new benchmark, Multi-Phase, Multi-Transition, and Multi-Scenery Video Object Segmentation (M^3-VOS), to verify the ability of models to understand object phases, which consists of 471 high-resolution videos spanning over 10 distinct everyday scenarios. It provides dense instance mask annotations that capture both object phases and their transitions. We evaluate state-of-the-art methods on M^3-VOS, yielding several key insights. Notably, current appearance-based approaches show significant room for improvement when handling objects with phase transitions. The inherent changes in disorder suggest that the predictive performance of the forward entropy-increasing process can be improved through a reverse entropy-reducing process. These findings lead us to propose ReVOS, a new plug-and-play model that improves its performance by reversal refinement. Our data and code will be publicly available at https://zixuan-chen.github.io/M-cube-VOS.github.io/. | [
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] |
62 | Self-Expansion of Pre-trained Models with Mixture of Adapters for Continual Learning | [
"Huiyi Wang",
"Haodong Lu",
"Lina Yao",
"Dong Gong"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Self-Expansion_of_Pre-trained_Models_with_Mixture_of_Adapters_for_Continual_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Self-Expansion_of_Pre-trained_Models_with_Mixture_of_Adapters_for_Continual_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Self-Expansion_of_Pre-trained_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2403.18886 | @InProceedings{Wang_2025_CVPR,
author = {Wang, Huiyi and Lu, Haodong and Yao, Lina and Gong, Dong},
title = {Self-Expansion of Pre-trained Models with Mixture of Adapters for Continual Learning},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {10087-10098}
} | Continual learning (CL) aims to continually accumulate knowledge from a non-stationary data stream without catastrophic forgetting of learned knowledge, requiring a balance between stability and adaptability. Relying on the generalizable representation in pre-trained models (PTMs), PTM-based CL methods perform effective continual adaptation on downstream tasks by adding learnable adapters or prompts upon the frozen PTMs. However, many existing PTM-based CL methods use restricted adaptation on a fixed set of these modules to avoid forgetting, suffering from limited CL ability. Periodically adding task-specific modules results in linear model growth rate and impaired knowledge reuse. We propose Self-Expansion of pre-trained models with Modularized Adaptation (SEMA), a novel approach to enhance the control of stability-plasticity balance in PTM-based CL. SEMA automatically decides to reuse or add adapter modules on demand in CL, depending on whether significant distribution shift that cannot be handled is detected at different representation levels. We design modular adapter consisting of a functional adapter and a representation descriptor. The representation descriptors are trained as a distribution shift indicator and used to trigger self-expansion signals. For better composing the adapters, an expandable weighting router is learned jointly for mixture of adapter outputs. SEMA enables better knowledge reuse and sub-linear expansion rate. Extensive experiments demonstrate the effectiveness of the proposed self-expansion method, achieving state-of-the-art performance compared to PTM-based CL methods without memory rehearsal. Code is available at https://github.com/huiyiwang01/SEMA-CL. | [
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] |
63 | Dual Prompting Image Restoration with Diffusion Transformers | [
"Dehong Kong",
"Fan Li",
"Zhixin Wang",
"Jiaqi Xu",
"Renjing Pei",
"Wenbo Li",
"WenQi Ren"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Kong_Dual_Prompting_Image_Restoration_with_Diffusion_Transformers_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Kong_Dual_Prompting_Image_Restoration_with_Diffusion_Transformers_CVPR_2025_paper.pdf | null | http://arxiv.org/abs/2504.17825 | @InProceedings{Kong_2025_CVPR,
author = {Kong, Dehong and Li, Fan and Wang, Zhixin and Xu, Jiaqi and Pei, Renjing and Li, Wenbo and Ren, WenQi},
title = {Dual Prompting Image Restoration with Diffusion Transformers},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {12809-12819}
} | Recent state-of-the-art image restoration methods mostly adopt latent diffusion models with U-Net backbones, yet still facing challenges in achieving high-quality restoration due to their limited capabilities. Diffusion transformers (DiTs), like SD3, are emerging as a promising alternative because of their better quality with scalability. However, previous conditional control methods for U-Net-based diffusion models, such as ControlNet, are not well-suited for DiTs. In this paper, we introduce DPIR (Dual Recent state-of-the-art image restoration methods mostly adopt latent diffusion models with U-Net backbones, yet still facing challenges in achieving high-quality restoration due to their limited capabilities. Diffusion transformers (DiTs), like SD3, are emerging as a promising alternative because of their better quality with scalability. In this paper, we introduce DPIR (Dual Prompting Image Restoration), a novel image restoration method that effectivly extracts conditional information of low-quality images from multiple perspectives. Specifically, DPIR consits of two branches: a low-quality image conditioning branch and a dual prompting control branch. The first branch utilizes a lightweight module to incorporate image priors into the DiT with high efficiency. More importantly, we believe that in image restoration, textual description alone cannot fully capture its rich visual characteristics. Therefore, a dual prompting module is designed to provide DiT with additional visual cues, capturing both global context and local appearance. The extracted global-local visual prompts as extra conditional control, alongside textual prompts to form dual prompts, greatly enhance the quality of the restoration. Extensive experimental results demonstrate that DPIR delivers superior image restoration performance. | [
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64 | Brain-Inspired Spiking Neural Networks for Energy-Efficient Object Detection | [
"Ziqi Li",
"Tao Gao",
"Yisheng An",
"Ting Chen",
"Jing Zhang",
"Yuanbo Wen",
"Mengkun Liu",
"Qianxi Zhang"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Li_Brain-Inspired_Spiking_Neural_Networks_for_Energy-Efficient_Object_Detection_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Brain-Inspired_Spiking_Neural_Networks_for_Energy-Efficient_Object_Detection_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Brain-Inspired_Spiking_Neural_CVPR_2025_supplemental.pdf | null | @InProceedings{Li_2025_CVPR,
author = {Li, Ziqi and Gao, Tao and An, Yisheng and Chen, Ting and Zhang, Jing and Wen, Yuanbo and Liu, Mengkun and Zhang, Qianxi},
title = {Brain-Inspired Spiking Neural Networks for Energy-Efficient Object Detection},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {3552-3562}
} | Brain-inspired spiking neural networks (SNNs) have the capability of energy-efficient processing of temporal information. However, leveraging the rich dynamic characteristics of SNNs and prior works in artificial neural networks (ANNs) to construct an effective object detection model for visual tasks remains an open question for further exploration. To develop a directly-trained , low energy consumption and high-performance multi-scale SNN model, we propose a novel interpretable object detection framework Multi-scale Spiking Detector (MSD). Initially, we propose a spiking convolutional neuron as a core component of the Optic Nerve Nucleus Block (ONNB), designed to significantly enhance the deep feature extraction capabilities of SNNs. ONNB enables direct training with improved energy efficiency, demonstrating superior performance compared to state-of-the-art ANN-to-SNN conversion and SNN techniques. In addition, we propose a Multi-scale Spiking Detection Framework to emulate the biological response and comprehension of stimuli from different objects. Wherein, spiking multi-scale fusion and the spiking detector are employed to integrate features across different depths and to detect response outcomes, respectively. Our method outperforms state-of-the-art ANN detectors, with only 7.8 M parameters and 6.43 mJ energy consumption. MSD obtains the mean average precision (mAP) of 62.0% and 66.3% on COCO and Gen1 datasets, respectively. | [
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65 | Medusa: A Multi-Scale High-order Contrastive Dual-Diffusion Approach for Multi-View Clustering | [
"Liang Chen",
"Zhe Xue",
"Yawen Li",
"Meiyu Liang",
"Yan Wang",
"Anton van den Hengel",
"Yuankai Qi"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Medusa_A_Multi-Scale_High-order_Contrastive_Dual-Diffusion_Approach_for_Multi-View_Clustering_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Medusa_A_Multi-Scale_High-order_Contrastive_Dual-Diffusion_Approach_for_Multi-View_Clustering_CVPR_2025_paper.pdf | null | null | @InProceedings{Chen_2025_CVPR,
author = {Chen, Liang and Xue, Zhe and Li, Yawen and Liang, Meiyu and Wang, Yan and van den Hengel, Anton and Qi, Yuankai},
title = {Medusa: A Multi-Scale High-order Contrastive Dual-Diffusion Approach for Multi-View Clustering},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {10295-10304}
} | Deep multi-view clustering methods utilize information from multiple views to achieve enhanced clustering results and have gained increasing popularity in recent years. Most existing methods typically focus on either inter-view or intra-view relationships, aiming to align information across views or analyze structural patterns within individual views. However, they often incorporate inter-view complementary information in a simplistic manner, while overlooking the complex, high-order relationships within multi-view data and the interactions among samples, resulting in an incomplete utilization of the rich information available. Instead, we propose a multi-scale approach that exploits all of the available information. We first introduce a dual graph diffusion module guided by a consensus graph. This module leverages inter-view information to enhance the representation of both nodes and edges within each view. Secondly, we propose a novel contrastive loss function based on hypergraphs to more effectively model and leverage complex intra-view data relationships. Finally, we propose to adaptively learn fusion weights at the sample level, which enables a more flexible and dynamic aggregation of multi-view information. Extensive experiments on eight datasets show favorable performance of the proposed method compared to state-of-the-art approaches, demonstrating its effectiveness across diverse scenarios. | [
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] |
66 | MambaOut: Do We Really Need Mamba for Vision? | [
"Weihao Yu",
"Xinchao Wang"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Yu_MambaOut_Do_We_Really_Need_Mamba_for_Vision_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_MambaOut_Do_We_Really_Need_Mamba_for_Vision_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yu_MambaOut_Do_We_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2405.07992 | @InProceedings{Yu_2025_CVPR,
author = {Yu, Weihao and Wang, Xinchao},
title = {MambaOut: Do We Really Need Mamba for Vision?},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {4484-4496}
} | Mamba, an architecture with RNN-like token mixer of state space model (SSM), was recently introduced to address the quadratic complexity of the attention mechanism and subsequently applied to vision tasks. Nevertheless, the performance of Mamba for vision is often underwhelming when compared with convolutional and attention-based models. In this paper, we delve into the essence of Mamba, and conceptually conclude that Mamba is ideally suited for tasks with long-sequence and autoregressive characteristics. For vision tasks, as image classification on ImageNet does not align with either characteristic, we hypothesize that Mamba is not necessary for this task; Detection and segmentation tasks on COCO or ADE20K are also not autoregressive, yet they adhere to the long-sequence characteristic, so we believe it is still worthwhile to explore Mamba's potential for these tasks. To empirically verify our hypotheses, we construct a series of models named MambaOut through stacking Mamba blocks while removing their core token mixer, SSM. Experimental results strongly support our hypotheses. Specifically, our MambaOut model surpasses all visual Mamba models on ImageNet image classification, indicating that Mamba is indeed unnecessary for this task. As for detection and segmentation, MambaOut cannot match the performance of state-of-the-art visual Mamba models, demonstrating the potential of Mamba for long-sequence visual tasks. | [
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67 | Everything to the Synthetic: Diffusion-driven Test-time Adaptation via Synthetic-Domain Alignment | [
"Jiayi Guo",
"Junhao Zhao",
"Chaoqun Du",
"Yulin Wang",
"Chunjiang Ge",
"Zanlin Ni",
"Shiji Song",
"Humphrey Shi",
"Gao Huang"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Guo_Everything_to_the_Synthetic_Diffusion-driven_Test-time_Adaptation_via_Synthetic-Domain_Alignment_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Guo_Everything_to_the_Synthetic_Diffusion-driven_Test-time_Adaptation_via_Synthetic-Domain_Alignment_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Guo_Everything_to_the_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2406.04295 | @InProceedings{Guo_2025_CVPR,
author = {Guo, Jiayi and Zhao, Junhao and Du, Chaoqun and Wang, Yulin and Ge, Chunjiang and Ni, Zanlin and Song, Shiji and Shi, Humphrey and Huang, Gao},
title = {Everything to the Synthetic: Diffusion-driven Test-time Adaptation via Synthetic-Domain Alignment},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {30503-30513}
} | Test-time adaptation (TTA) aims to improve the performance of source-domain pre-trained models on previously unseen, shifted target domains. Traditional TTA methods primarily adapt model weights based on target data streams, making model performance sensitive to the amount and order of target data. The recently proposed diffusion-driven TTA methods mitigate this by adapting model inputs instead of weights, where an unconditional diffusion model, trained on the source domain, transforms target-domain data into a synthetic domain that is expected to approximate the source domain. However, in this paper, we reveal that although the synthetic data in diffusion-driven TTA seems indistinguishable from the source data, it is unaligned with, or even markedly different from the latter for deep networks. To address this issue, we propose a Synthetic-Domain Alignment (SDA) framework. Our key insight is to fine-tune the source model with synthetic data to ensure better alignment. Specifically, we first employ a conditional diffusion model to generate labeled samples, creating a synthetic dataset. Subsequently, we use the aforementioned unconditional diffusion model to add noise to and denoise each sample before fine-tuning. This Mix of Diffusion (MoD) process mitigates the potential domain misalignment between the conditional and unconditional models. Extensive experiments across classifiers, segmenters, and multimodal large language models (MLLMs, e.g., LLaVA) demonstrate that SDA achieves superior domain alignment and consistently outperforms existing diffusion-driven TTA methods. Our code is available at https://github.com/SHI-Labs/Diffusion-Driven-Test-Time-Adaptation-via-Synthetic-Domain-Alignment. | [
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68 | Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation | [
"Peihua Deng",
"Jiehua Zhang",
"Xichun Sheng",
"Chenggang Yan",
"Yaoqi Sun",
"Ying Fu",
"Liang Li"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Deng_Multi-Granularity_Class_Prototype_Topology_Distillation_for_Class-Incremental_Source-Free_Unsupervised_Domain_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Deng_Multi-Granularity_Class_Prototype_Topology_Distillation_for_Class-Incremental_Source-Free_Unsupervised_Domain_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Deng_Multi-Granularity_Class_Prototype_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2411.16064 | @InProceedings{Deng_2025_CVPR,
author = {Deng, Peihua and Zhang, Jiehua and Sheng, Xichun and Yan, Chenggang and Sun, Yaoqi and Fu, Ying and Li, Liang},
title = {Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {30566-30576}
} | This paper explores the Class-Incremental Source-Free Unsupervised Domain Adaptation (CI-SFUDA) problem, where the unlabeled target data come incrementally without access to labeled source instances. This problem poses two challenges, the interference of similar source-class knowledge in target-class representation learning and the shocks of new target knowledge to old ones. To address them, we propose the Multi-Granularity Class Prototype Topology Distillation (GROTO) algorithm, which effectively transfers the source knowledge to the class-incremental target domain. Concretely, we design the multi-granularity class prototype self-organization module and the prototype topology distillation module. First, we mine the positive classes by modeling accumulation distributions. Next, we introduce multi-granularity class prototypes to generate reliable pseudo-labels, and exploit them to promote the positive-class target feature self-organization. Second, the positive-class prototypes are leveraged to construct the topological structures of source and target feature spaces. Then, we perform the topology distillation to continually mitigate the shocks of new target knowledge to old ones. Extensive experiments demonstrate that our proposed method achieves state-of-the-art performance on three public datasets. | [
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69 | DepthCues: Evaluating Monocular Depth Perception in Large Vision Models | [
"Duolikun Danier",
"Mehmet Aygün",
"Changjian Li",
"Hakan Bilen",
"Oisin Mac Aodha"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Danier_DepthCues_Evaluating_Monocular_Depth_Perception_in_Large_Vision_Models_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Danier_DepthCues_Evaluating_Monocular_Depth_Perception_in_Large_Vision_Models_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Danier_DepthCues_Evaluating_Monocular_CVPR_2025_supplemental.pdf | null | @InProceedings{Danier_2025_CVPR,
author = {Danier, Duolikun and Ayg\"un, Mehmet and Li, Changjian and Bilen, Hakan and Mac Aodha, Oisin},
title = {DepthCues: Evaluating Monocular Depth Perception in Large Vision Models},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {20049-20059}
} | Large-scale pre-trained vision models are becoming increasingly prevalent, offering expressive and generalizable visual representations that benefit various downstream tasks. Recent studies on the emergent properties of these models have revealed their high-level geometric understanding, in particular in the context of depth perception. However, it remains unclear how depth perception arises in these models without explicit depth supervision provided during pre-training. To investigate this, we examine whether the monocular depth cues, similar to those used by the human visual system, emerge in these models. We introduce a new benchmark, DepthCues, designed to evaluate depth cue understanding, and present findings across 20 diverse and representative pre-trained vision models. Our analysis shows that human-like depth cues emerge in more recent larger models. We also explore enhancing depth perception in large vision models by fine-tuning on DepthCues, and find that even without dense depth supervision, this improves depth estimation. To support further research, our benchmark and evaluation code will be made publicly available for studying depth perception in vision models. | [
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70 | A Polarization-Aided Transformer for Image Deblurring via Motion Vector Decomposition | [
"Duosheng Chen",
"Shihao Zhou",
"Jinshan Pan",
"Jinglei Shi",
"Lishen Qu",
"Jufeng Yang"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Chen_A_Polarization-Aided_Transformer_for_Image_Deblurring_via_Motion_Vector_Decomposition_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_A_Polarization-Aided_Transformer_for_Image_Deblurring_via_Motion_Vector_Decomposition_CVPR_2025_paper.pdf | null | null | @InProceedings{Chen_2025_CVPR,
author = {Chen, Duosheng and Zhou, Shihao and Pan, Jinshan and Shi, Jinglei and Qu, Lishen and Yang, Jufeng},
title = {A Polarization-Aided Transformer for Image Deblurring via Motion Vector Decomposition},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {28061-28070}
} | Effectively leveraging motion information is crucial for the image deblurring task. Existing methods typically build deep-learning models to restore a clean image by estimating blur patterns over the entire movement. This suggests that the blur caused by rotational motion components is processed together with the translational one. Exploring the movement without separation leads to limited performance for complex motion deblurring, especially rotational motion. In this paper, we propose Motion Decomposition Transformer (MDT), a transformer-based architecture augmented with polarized modules for deblurring via motion vector decomposition. MDT consists of a Motion Decomposition Module (MDM) for extracting hybrid rotation and translation features, and a Radial Stripe Attention Solver (RSAS) for sharp image reconstruction with enhanced rotational information. Specifically, the MDM uses a deformable Cartesian convolutional branch to capture translational motion, complemented by a polar-system branch to capture rotational motion. The RSAS employs radial stripe windows and angular relative positional encoding in the polar system to enhance rotational information. This design preserves translational details while keeping computational costs lower than dual-coordinate design. Experimental results on 6 image deblurring datasets show that MDT outperforms state-of-the-art methods, particularly in handling blur caused by complex motions with significant rotational components. | [
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71 | SpecTRe-GS: Modeling Highly Specular Surfaces with Reflected Nearby Objects by Tracing Rays in 3D Gaussian Splatting | [
"Jiajun Tang",
"Fan Fei",
"Zhihao Li",
"Xiao Tang",
"Shiyong Liu",
"Youyu Chen",
"Binxiao Huang",
"Zhenyu Chen",
"Xiaofei Wu",
"Boxin Shi"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Tang_SpecTRe-GS_Modeling_Highly_Specular_Surfaces_with_Reflected_Nearby_Objects_by_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Tang_SpecTRe-GS_Modeling_Highly_Specular_Surfaces_with_Reflected_Nearby_Objects_by_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tang_SpecTRe-GS_Modeling_Highly_CVPR_2025_supplemental.pdf | null | @InProceedings{Tang_2025_CVPR,
author = {Tang, Jiajun and Fei, Fan and Li, Zhihao and Tang, Xiao and Liu, Shiyong and Chen, Youyu and Huang, Binxiao and Chen, Zhenyu and Wu, Xiaofei and Shi, Boxin},
title = {SpecTRe-GS: Modeling Highly Specular Surfaces with Reflected Nearby Objects by Tracing Rays in 3D Gaussian Splatting},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {16133-16142}
} | 3D Gaussian Splatting (3DGS), a recently emerged multi-view 3D reconstruction technique, has shown significant advantages in real-time rendering and explicit editing. However, 3DGS encounters challenges in the accurate modeling of both high-frequency view-dependent appearances and global illumination effects, including inter-reflection. This paper introduces SpecTRe-GS, which addresses these challenges and models highly Specular surfaces that reflect nearby objects through Tracing Rays in 3D Gaussian Splatting. SpecTRe-GS separately models reflections from highly specular and rough surfaces to leverage the distinctions between their reflective properties and integrates an efficient ray tracer within the 3DGS framework for querying secondary rays, thus achieving fast and accurate rendering. Also, it incorporates normal prior guidance and joint geometry optimization at various stages of the training process to enhance geometry reconstruction for undistorted reflections. Experiments on both synthetic and real-world scenes demonstrate the superiority of SpecTRe-GS compared to existing 3DGS-based methods in capturing highly specular inter-reflections and also showcase its editing applications. | [
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] |
72 | Seurat: From Moving Points to Depth | [
"Seokju Cho",
"Jiahui Huang",
"Seungryong Kim",
"Joon-Young Lee"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Cho_Seurat_From_Moving_Points_to_Depth_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Cho_Seurat_From_Moving_Points_to_Depth_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cho_Seurat_From_Moving_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2504.14687 | @InProceedings{Cho_2025_CVPR,
author = {Cho, Seokju and Huang, Jiahui and Kim, Seungryong and Lee, Joon-Young},
title = {Seurat: From Moving Points to Depth},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {7211-7221}
} | Accurate depth estimation from monocular videos remains challenging due to ambiguities inherent in single-view geometry, as crucial depth cues like stereopsis are absent. However, humans often perceive relative depth intuitively by observing variations in the size and spacing of objects as they move. Inspired by this, we propose a novel method that infers relative depth by examining the spatial relationships and temporal evolution of a set of tracked 2D trajectories. Specifically, we use off-the-shelf point tracking models to capture 2D trajectories. Then, our approach employs spatial and temporal transformers to process these trajectories and directly infer depth changes over time. Evaluated on the TAPVid-3D benchmark, our method demonstrates robust zero-shot performance, generalizing effectively from synthetic to real-world datasets. Results indicate that our approach achieves temporally smooth, high-accuracy depth predictions across diverse domains. | [
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73 | AuraFusion360: Augmented Unseen Region Alignment for Reference-based 360deg Unbounded Scene Inpainting | [
"Chung-Ho Wu",
"Yang-Jung Chen",
"Ying-Huan Chen",
"Jie-Ying Lee",
"Bo-Hsu Ke",
"Chun-Wei Tuan Mu",
"Yi-Chuan Huang",
"Chin-Yang Lin",
"Min-Hung Chen",
"Yen-Yu Lin",
"Yu-Lun Liu"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Wu_AuraFusion360_Augmented_Unseen_Region_Alignment_for_Reference-based_360deg_Unbounded_Scene_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_AuraFusion360_Augmented_Unseen_Region_Alignment_for_Reference-based_360deg_Unbounded_Scene_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_AuraFusion360_Augmented_Unseen_CVPR_2025_supplemental.pdf | null | @InProceedings{Wu_2025_CVPR,
author = {Wu, Chung-Ho and Chen, Yang-Jung and Chen, Ying-Huan and Lee, Jie-Ying and Ke, Bo-Hsu and Mu, Chun-Wei Tuan and Huang, Yi-Chuan and Lin, Chin-Yang and Chen, Min-Hung and Lin, Yen-Yu and Liu, Yu-Lun},
title = {AuraFusion360: Augmented Unseen Region Alignment for Reference-based 360deg Unbounded Scene Inpainting},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {16366-16376}
} | Three-dimensional scene inpainting is crucial for applications from virtual reality to architectural visualization, yet existing methods struggle with view consistency and geometric accuracy in 360deg unbounded scenes. We present AuraFusion360, a novel reference-based method that enables high-quality object removal and hole filling in 3D scenes represented by Gaussian Splatting. Our approach introduces (1) depth-aware unseen mask generation for accurate occlusion identification, (2) Adaptive Guided Depth Diffusion, a zero-shot method for accurate initial point placement without requiring additional training, and (3) SDEdit-based detail enhancement for multi-view coherence. We also introduce 360-USID, the first comprehensive dataset for 360deg unbounded scene inpainting with ground truth. Extensive experiments demonstrate that AuraFusion360 significantly outperforms existing methods, achieving superior perceptual quality while maintaining geometric accuracy across dramatic viewpoint changes. | [
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] |
74 | Language-Guided Image Tokenization for Generation | [
"Kaiwen Zha",
"Lijun Yu",
"Alireza Fathi",
"David A. Ross",
"Cordelia Schmid",
"Dina Katabi",
"Xiuye Gu"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Zha_Language-Guided_Image_Tokenization_for_Generation_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Zha_Language-Guided_Image_Tokenization_for_Generation_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zha_Language-Guided_Image_Tokenization_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2412.05796 | @InProceedings{Zha_2025_CVPR,
author = {Zha, Kaiwen and Yu, Lijun and Fathi, Alireza and Ross, David A. and Schmid, Cordelia and Katabi, Dina and Gu, Xiuye},
title = {Language-Guided Image Tokenization for Generation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {15713-15722}
} | Image tokenization, the process of transforming raw image pixels into a compact low-dimensional latent representation, has proven crucial for scalable and efficient image generation. However, mainstream image tokenization methods generally have limited compression rates, making high-resolution image generation computationally expensive. To address this challenge, we propose to leverage language for efficient image tokenization, and we call our method Text-Conditioned Image Tokenization (TexTok). TexTok is a simple yet effective tokenization framework that leverages language to provide a compact, high-level semantic representation. By conditioning the tokenization process on descriptive text captions, TexTok simplifies semantic learning, allowing more learning capacity and token space to be allocated to capture fine-grained visual details, leading to enhanced reconstruction quality and higher compression rates. Compared to the conventional tokenizer without text conditioning, TexTok achieves average reconstruction FID improvements of 29.2% and 48.1% on ImageNet-256 and -512 benchmarks respectively, across varying numbers of tokens. These tokenization improvements consistently translate to 16.3% and 34.3% average improvements in generation FID. By simply replacing the tokenizer in Diffusion Transformer (DiT) with TexTok, our system can achieve a 93.5x inference speedup while still outperforming the original DiT using only 32 tokens on ImageNet-512. TexTok with a vanilla DiT generator achieves state-of-the-art FID scores of 1.46 and 1.62 on ImageNet-256 and -512 respectively. Furthermore, we demonstrate TexTok's superiority on the text-to-image generation task, effectively utilizing the off-the-shelf text captions in tokenization. | [
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75 | Img-Diff: Contrastive Data Synthesis for Multimodal Large Language Models | [
"Qirui Jiao",
"Daoyuan Chen",
"Yilun Huang",
"Bolin Ding",
"Yaliang Li",
"Ying Shen"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Jiao_Img-Diff_Contrastive_Data_Synthesis_for_Multimodal_Large_Language_Models_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Jiao_Img-Diff_Contrastive_Data_Synthesis_for_Multimodal_Large_Language_Models_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jiao_Img-Diff_Contrastive_Data_CVPR_2025_supplemental.pdf | null | @InProceedings{Jiao_2025_CVPR,
author = {Jiao, Qirui and Chen, Daoyuan and Huang, Yilun and Ding, Bolin and Li, Yaliang and Shen, Ying},
title = {Img-Diff: Contrastive Data Synthesis for Multimodal Large Language Models},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {9296-9307}
} | High-performance Multimodal Large Language Models (MLLMs) rely heavily on data quality. This study introduces a novel data synthesis method, leveraging insights from contrastive learning and image difference captioning to enhance fine-grained image recognition in MLLMs. By analyzing object differences in detailed regions between similar images, we challenge the model to identify both matching and distinct components. Specifically, our method initially create pairs of similar images that highlight object variations. After that, we introduce a Difference Area Generator for object differences identifying, followed by a Difference Captions Generator for differences describing. The outcome is a high-quality dataset of "object replacement" samples, named Img-Diff, which can be expanded as needed due to its automation. We use the generated dataset to finetune state-of-the-art (SOTA) MLLMs such as InternVL2, yielding comprehensive improvements across numerous image difference and Visual Question Answering tasks. For instance, the trained models notably surpass the SOTA models GPT-4V and Gemini on the MMVP benchmark. Additionally, we conduct thorough evaluations to confirm the dataset's diversity, quality, and robustness, presenting several insights on the synthesis of such a contrastive dataset. We release our codes and dataset to encourage further research on multimodal data synthesis and MLLMs' fundamental capabilities for image understanding. | [
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76 | CocoER: Aligning Multi-Level Feature by Competition and Coordination for Emotion Recognition | [
"Xuli Shen",
"Hua Cai",
"Weilin Shen",
"Qing Xu",
"Dingding Yu",
"Weifeng Ge",
"Xiangyang Xue"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Shen_CocoER_Aligning_Multi-Level_Feature_by__Competition_and_Coordination_for_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Shen_CocoER_Aligning_Multi-Level_Feature_by__Competition_and_Coordination_for_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shen_CocoER_Aligning_Multi-Level_CVPR_2025_supplemental.pdf | null | @InProceedings{Shen_2025_CVPR,
author = {Shen, Xuli and Cai, Hua and Shen, Weilin and Xu, Qing and Yu, Dingding and Ge, Weifeng and Xue, Xiangyang},
title = {CocoER: Aligning Multi-Level Feature by Competition and Coordination for Emotion Recognition},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {29591-29600}
} | With the explosion of human-machine interaction, emotion recognition has reignited attention. Previous works focus on improving visual feature fusion and reasoning from multiple image levels. Although it is non-trivial to deduce a person's emotion by integrating multi-level feature (head, body and context), the emotion recognition results of each level is usually different from one another, which creates inconsistency in the prevailing feature alignment method and decrease recognition performance. In this work, we propose a multi-level image feature refinement method for emotion recognition (CocoER) to mitigate the impact caused by conflicting results from multi-level recognition. First, we leverage cross-level attention to improve visual feature consistency between hierarchically cropped head, body and context windows. Then, vocabulary informed alignment is incorporated into the recognition framework to produce pseudo label and guide hierarchical visual feature refinement. To effectively fuse multi-level feature, we elaborate on a competition process of eliminating irrelevant image level predictions and a coordination process to enhance the feature across all levels. Extensive experiments are executed on two popular datasets, and our method achieves state-of-the-art performance with multi-level interpretation results. | [
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] |
77 | Hyperbolic Uncertainty-Aware Few-Shot Incremental Point Cloud Segmentation | [
"Tanuj Sur",
"Samrat Mukherjee",
"Kaizer Rahaman",
"Subhasis Chaudhuri",
"Muhammad Haris Khan",
"Biplab Banerjee"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Sur_Hyperbolic_Uncertainty-Aware_Few-Shot_Incremental_Point_Cloud_Segmentation_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Sur_Hyperbolic_Uncertainty-Aware_Few-Shot_Incremental_Point_Cloud_Segmentation_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sur_Hyperbolic_Uncertainty-Aware_Few-Shot_CVPR_2025_supplemental.pdf | null | @InProceedings{Sur_2025_CVPR,
author = {Sur, Tanuj and Mukherjee, Samrat and Rahaman, Kaizer and Chaudhuri, Subhasis and Khan, Muhammad Haris and Banerjee, Biplab},
title = {Hyperbolic Uncertainty-Aware Few-Shot Incremental Point Cloud Segmentation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {11810-11821}
} | 3D point cloud segmentation is essential across a range of applications; however, conventional methods often struggle in evolving environments, particularly when tasked with identifying novel categories under limited supervision. Few-Shot Learning (FSL) and Class Incremental Learning (CIL) have been adapted previously to address these challenges in isolation, yet the combined paradigm of Few-Shot Class Incremental Learning (FSCIL) remains largely unexplored for point cloud segmentation. To address this gap, we introduce Hyperbolic Ideal Prototypes Optimization (HIPO), a novel framework that harnesses hyperbolic embeddings for FSCIL in 3D point clouds. HIPO employs the Poincare Hyperbolic Sphere as its embedding space, integrating Ideal Prototypes enriched by CLIP-derived class semantics, to capture the hierarchical structure of 3D data. By enforcing orthogonality among prototypes and maximizing representational margins, HIPO constructs a resilient embedding space that mitigates forgetting and enables the seamless integration of new classes, thereby effectively countering overfitting. Extensive evaluations on S3DIS, ScanNetv2, and cross-dataset scenarios demonstrate HIPO's strong performance, significantly surpassing existing approaches in both in-domain and cross-dataset FSCIL tasks for 3D point cloud segmentation. | [
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] |
78 | Enhancing Creative Generation on Stable Diffusion-based Models | [
"Jiyeon Han",
"Dahee Kwon",
"Gayoung Lee",
"Junho Kim",
"Jaesik Choi"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Han_Enhancing_Creative_Generation_on_Stable_Diffusion-based_Models_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Han_Enhancing_Creative_Generation_on_Stable_Diffusion-based_Models_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Han_Enhancing_Creative_Generation_CVPR_2025_supplemental.zip | http://arxiv.org/abs/2503.23538 | @InProceedings{Han_2025_CVPR,
author = {Han, Jiyeon and Kwon, Dahee and Lee, Gayoung and Kim, Junho and Choi, Jaesik},
title = {Enhancing Creative Generation on Stable Diffusion-based Models},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {28609-28618}
} | Recent text-to-image generative models, particularly Stable Diffusion and its distilled variants, have achieved impressive fidelity and strong text-image alignment. However, their creative generation capacity remains limited, as simply adding the term "creative" to prompts often fails to yield genuinely creative results. In this paper, we introduce C3 (Creative Concept Catalyst), a training-free approach designed to enhance creativity in Stable Diffusion-based models. C3 selectively amplifies features during the denoising process to foster more creative outputs. We offer practical guidelines for choosing amplification factors based on two main aspects of creativity. C3 allows user-friendly creativity control in image generation and is the first study to enhance creativity in diffusion models without extensive computational costs. We demonstrate its effectiveness across various Stable Diffusion-based models. Source codes will be publicly available. | [
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79 | The Devil is in the Prompts: Retrieval-Augmented Prompt Optimization for Text-to-Video Generation | [
"Bingjie Gao",
"Xinyu Gao",
"Xiaoxue Wu",
"Yujie Zhou",
"Yu Qiao",
"Li Niu",
"Xinyuan Chen",
"Yaohui Wang"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Gao_The_Devil_is_in_the_Prompts_Retrieval-Augmented_Prompt_Optimization_for_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Gao_The_Devil_is_in_the_Prompts_Retrieval-Augmented_Prompt_Optimization_for_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gao_The_Devil_is_CVPR_2025_supplemental.zip | http://arxiv.org/abs/2504.11739 | @InProceedings{Gao_2025_CVPR,
author = {Gao, Bingjie and Gao, Xinyu and Wu, Xiaoxue and Zhou, Yujie and Qiao, Yu and Niu, Li and Chen, Xinyuan and Wang, Yaohui},
title = {The Devil is in the Prompts: Retrieval-Augmented Prompt Optimization for Text-to-Video Generation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {3173-3183}
} | The evolution of Text-to-video (T2V) generative models, trained on large-scale datasets, has been marked by significant progress. However, the sensitivity of T2V generative models to input prompts highlights the critical role of prompt design in influencing generative outcomes. Prior research has predominantly relied on Large Language Models (LLMs) to align user-provided prompts with the distribution of training prompts, albeit without tailored guidance encompassing prompt vocabulary and sentence structure nuances. To this end, we introduce RAPO, a novel Retrieval-Augmented Prompt Optimization framework. In order to address potential inaccuracies and ambiguous details generated by LLM-generated prompts. RAPO refines the naive prompts through dual optimization branches, selecting the superior prompt for T2V generation. The first branch augments user prompts with diverse modifiers extracted from a learned relational graph, refining them to align with the format of training prompts via a fine-tuned LLM. Conversely, the second branch rewrites the naive prompt using a pre-trained LLM following a well-defined instruction set. Extensive experiments demonstrate that RAPO can effectively enhance both the static and dynamic dimensions of generated videos, demonstrating the significance of prompt optimization for user-provided prompts. Project website: \href https://whynothaha.github.io/Prompt_optimizer/RAPO.html GitHub . | [
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] |
80 | Denoising Functional Maps: Diffusion Models for Shape Correspondence | [
"Aleksei Zhuravlev",
"Zorah Lähner",
"Vladislav Golyanik"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Zhuravlev_Denoising_Functional_Maps_Diffusion_Models_for_Shape_Correspondence_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Zhuravlev_Denoising_Functional_Maps_Diffusion_Models_for_Shape_Correspondence_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhuravlev_Denoising_Functional_Maps_CVPR_2025_supplemental.pdf | null | @InProceedings{Zhuravlev_2025_CVPR,
author = {Zhuravlev, Aleksei and L\"ahner, Zorah and Golyanik, Vladislav},
title = {Denoising Functional Maps: Diffusion Models for Shape Correspondence},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {26899-26909}
} | Estimating correspondences between pairs of deformable shapes remains a challenging problem. Despite substantial progress, existing methods lack broad generalization capabilities and require category-specific training data. To address these limitations, we propose a fundamentally new approach to shape correspondence based on denoising diffusion models. In our method, a diffusion model learns to directly predict the functional map, a low-dimensional representation of a point-wise map between shapes. We use a large dataset of synthetic human meshes for training and employ two steps to reduce the number of functional maps that need to be learned. First, the maps refer to a template rather than shape pairs. Second, the functional map is defined in a basis of eigenvectors of the Laplacian, which is not unique due to sign ambiguity. Therefore, we introduce an unsupervised approach to select a specific basis by correcting the signs of eigenvectors based on surface features. Our model achieves competitive performance on standard human datasets, meshes with anisotropic connectivity, non-isometric humanoid shapes, as well as animals compared to existing descriptor-based and large-scale shape deformation methods. | [
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] |
81 | ProReflow: Progressive Reflow with Decomposed Velocity | [
"Lei Ke",
"Haohang Xu",
"Xuefei Ning",
"Yu Li",
"Jiajun Li",
"Haoling Li",
"Yuxuan Lin",
"Dongsheng Jiang",
"Yujiu Yang",
"Linfeng Zhang"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Ke_ProReflow_Progressive_Reflow_with_Decomposed_Velocity_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Ke_ProReflow_Progressive_Reflow_with_Decomposed_Velocity_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ke_ProReflow_Progressive_Reflow_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.04824 | @InProceedings{Ke_2025_CVPR,
author = {Ke, Lei and Xu, Haohang and Ning, Xuefei and Li, Yu and Li, Jiajun and Li, Haoling and Lin, Yuxuan and Jiang, Dongsheng and Yang, Yujiu and Zhang, Linfeng},
title = {ProReflow: Progressive Reflow with Decomposed Velocity},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {28029-28038}
} | Diffusion models have achieved significant progress in both image and video generation while still suffering from huge computation costs. As an effective solution, rectified flow aims to rectify the diffusion process of diffusion models into a straight line for few-step and even one-step generation. However, in this paper, we suggest that the original training pipeline of reflow is not optimal and introduce two techniques to improve it. Firstly, we introduce progressive reflow, which progressively reflows the diffusion models in local timesteps until the whole diffusion progresses, reducing the difficulty of flow matching. Second, we introduce aligned v-prediction, which highlights the importance of direction matching in flow matching over magnitude matching. Experimental results on SDv1.5 and SDXL demonstrate the effectiveness of our method, for example, conducting on SDv1.5 achieves an FID of 10.70 on MSCOCO2014 validation set with only 4 sampling steps, close to our teacher model (32 DDIM steps, FID = 10.05). Our codes will be released at Github. | [
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82 | DnLUT: Ultra-Efficient Color Image Denoising via Channel-Aware Lookup Tables | [
"Sidi Yang",
"Binxiao Huang",
"Yulun Zhang",
"Dahai Yu",
"Yujiu Yang",
"Ngai Wong"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Yang_DnLUT_Ultra-Efficient_Color_Image_Denoising_via_Channel-Aware_Lookup_Tables_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_DnLUT_Ultra-Efficient_Color_Image_Denoising_via_Channel-Aware_Lookup_Tables_CVPR_2025_paper.pdf | null | http://arxiv.org/abs/2503.15931 | @InProceedings{Yang_2025_CVPR,
author = {Yang, Sidi and Huang, Binxiao and Zhang, Yulun and Yu, Dahai and Yang, Yujiu and Wong, Ngai},
title = {DnLUT: Ultra-Efficient Color Image Denoising via Channel-Aware Lookup Tables},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {7582-7591}
} | While deep neural networks have revolutionized image denoising capabilities, their deployment on edge devices remains challenging due to substantial computational and memory requirements. To this end, we present DnLUT, an ultra-efficient lookup table-based framework that achieves high-quality color image denoising with minimal resource consumption. Our key innovation lies in two complementary components: a Pairwise Channel Mixer (PCM) that effectively captures inter-channel correlations and spatial dependencies in parallel, and a novel L-shaped convolution design that maximizes receptive field coverage while minimizing storage overhead. By converting these components into optimized lookup tables post-training, DnLUT achieves remarkable efficiency - requiring only 500KB storage and 0.1% energy consumption compared to its CNN contestant DnCNN, while delivering 20x faster inference. Extensive experiments demonstrate that DnLUT outperforms all existing LUT-based methods by over 1dB in PSNR, establishing a new state-of-the-art in resource-efficient color image denoising. | [
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83 | Devil is in the Detail: Towards Injecting Fine Details of Image Prompt in Image Generation via Conflict-free Guidance and Stratified Attention | [
"Kyungmin Jo",
"Jooyeol Yun",
"Jaegul Choo"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Jo_Devil_is_in_the_Detail_Towards_Injecting_Fine_Details_of_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Jo_Devil_is_in_the_Detail_Towards_Injecting_Fine_Details_of_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jo_Devil_is_in_CVPR_2025_supplemental.pdf | null | @InProceedings{Jo_2025_CVPR,
author = {Jo, Kyungmin and Yun, Jooyeol and Choo, Jaegul},
title = {Devil is in the Detail: Towards Injecting Fine Details of Image Prompt in Image Generation via Conflict-free Guidance and Stratified Attention},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {23595-23603}
} | While large-scale text-to-image diffusion models enable the generation of high-quality, diverse images from text prompts, these prompts struggle to capture intricate details, such as textures, preventing the user intent from being reflected. This limitation has led to efforts to generate images conditioned on user-provided images, referred to as image prompts. Recent work modifies the self-attention mechanism to impose image conditions in generated images by replacing or concatenating the keys and values from the image prompt. This enables the self-attention layer to work like a cross-attention layer, generally used to incorporate text prompts.In this paper, we identify two common issues in existing methods of modifying self-attention that hinder diffusion models from reflecting the image prompt. By addressing these issues, we propose a novel method that generates images that properly reflect the details of image prompts. First, existing approaches often neglect the importance of image prompts in classifier-free guidance, which directs the model towards the intended conditions and away from those undesirable. Specifically, current methods use image prompts as both desired and undesired conditions, causing conflicting signals. To resolve this, we propose conflict-free guidance by using image prompts only as desired conditions, ensuring that the generated image faithfully reflects the image prompt.In addition, we observe that the two most common self-attention modifications involve a trade-off between the realism of the generated image and alignment with the image prompt, achieved by selectively using keys and values from both images. Specifically, selecting more keys and values from the image prompt improves alignment, while selecting more from the generated image enhances realism. To balance both, we propose an alternative self-attention modification method, Stratified Attention, which jointly uses keys and values from both images rather than selecting between them.Through extensive experiments across three distinct image generation tasks, we demonstrate that the proposed method outperforms existing image-prompting models in faithfully reflecting the image prompt. | [
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84 | D^3-Human: Dynamic Disentangled Digital Human from Monocular Video | [
"Honghu Chen",
"Bo Peng",
"Yunfan Tao",
"Juyong Zhang"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Chen_D3-Human_Dynamic_Disentangled_Digital_Human_from_Monocular_Video_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_D3-Human_Dynamic_Disentangled_Digital_Human_from_Monocular_Video_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_D3-Human_Dynamic_Disentangled_CVPR_2025_supplemental.pdf | null | @InProceedings{Chen_2025_CVPR,
author = {Chen, Honghu and Peng, Bo and Tao, Yunfan and Zhang, Juyong},
title = {D{\textasciicircum}3-Human: Dynamic Disentangled Digital Human from Monocular Video},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {10836-10846}
} | We introduce \text D ^3\text -Human , a method for reconstructing Dynamic Disentangled Digital Human geometry from monocular videos. Past monocular video human reconstruction primarily focuses on reconstructing undecoupled clothed human bodies or only reconstructing clothing, making it difficult to apply directly in applications such as animation production. The challenge in reconstructing decoupled clothing and body lies in the occlusion caused by clothing over the body. To this end, the details of the visible area and the plausibility of the invisible area must be ensured during the reconstruction process. Our proposed method combines explicit and implicit representations to model the decoupled clothed human body, leveraging the robustness of explicit representations and the flexibility of implicit representations. Specifically, we reconstruct the visible region as SDF and propose a novel human manifold signed distance field (hmSDF) to segment the visible clothing and visible body, and then merge the visible and invisible body. Extensive experimental results demonstrate that, compared with existing reconstruction schemes, \text D ^3\text -Human can achieve high-quality decoupled reconstruction of the human body wearing different clothing, and can be directly applied to clothing transfer and animation production. | [
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] |
85 | BiM-VFI: Bidirectional Motion Field-Guided Frame Interpolation for Video with Non-uniform Motions | [
"Wonyong Seo",
"Jihyong Oh",
"Munchurl Kim"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Seo_BiM-VFI_Bidirectional_Motion_Field-Guided_Frame_Interpolation_for_Video_with_Non-uniform_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Seo_BiM-VFI_Bidirectional_Motion_Field-Guided_Frame_Interpolation_for_Video_with_Non-uniform_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Seo_BiM-VFI_Bidirectional_Motion_CVPR_2025_supplemental.pdf | null | @InProceedings{Seo_2025_CVPR,
author = {Seo, Wonyong and Oh, Jihyong and Kim, Munchurl},
title = {BiM-VFI: Bidirectional Motion Field-Guided Frame Interpolation for Video with Non-uniform Motions},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {7244-7253}
} | Existing Video Frame interpolation (VFI) models tend to suffer from time-to-location ambiguity when trained with video of non-uniform motions, such as accelerating, decelerating, and changing directions, which often yield blurred interpolated frames.In this paper, we propose (i) a novel motion description map, Bidirectional Motion field (BiM), to effectively describe non-uniform motions; (ii) a BiM-guided Flow Net (BiMFN) with Content-Aware Upsampling Network (CAUN) for precise optical flow estimation; and (iii) Knowledge Distillation for VFI-centric Flow supervision (KDVCF) to supervise the motion estimation of VFI model with VFI-centric teacher flows.The proposed VFI is called a Bidirectional Motion field-guided VFI (BiM-VFI) model.Extensive experiments show that our BiM-VFI model significantly surpasses the recent state-of-the-art VFI methods by 26% and 45% improvements in LPIPS and STLPIPS respectively, yielding interpolated frames with much fewer blurs at arbitrary time instances. | [
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86 | Curriculum Coarse-to-Fine Selection for High-IPC Dataset Distillation | [
"Yanda Chen",
"Gongwei Chen",
"Miao Zhang",
"Weili Guan",
"Liqiang Nie"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Curriculum_Coarse-to-Fine_Selection_for_High-IPC_Dataset_Distillation_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Curriculum_Coarse-to-Fine_Selection_for_High-IPC_Dataset_Distillation_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Curriculum_Coarse-to-Fine_Selection_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.18872 | @InProceedings{Chen_2025_CVPR,
author = {Chen, Yanda and Chen, Gongwei and Zhang, Miao and Guan, Weili and Nie, Liqiang},
title = {Curriculum Coarse-to-Fine Selection for High-IPC Dataset Distillation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {20437-20446}
} | Dataset distillation (DD) excels in synthesizing a small number of images per class (IPC) but struggles to maintain its effectiveness in high-IPC settings. Recent works on dataset distillation demonstrate that combining distilled and real data can mitigate the effectiveness decay. However, our analysis of the combination paradigm reveals that the current one-shot and independent selection mechanism induces an incompatibility issue between distilled and real images. To address this issue, we introduce a novel curriculum coarse-to-fine selection (CCFS) method for efficient high-IPC dataset distillation. CCFS employs a curriculum selection framework for real data selection, where we leverage a coarse-to-fine strategy to select appropriate real data based on the current synthetic dataset in each curriculum. Extensive experiments validate CCFS, surpassing the state-of-the-art by +6.6% on CIFAR-10, +5.8% on CIFAR-100, and +3.4% on Tiny-ImageNet under high-IPC settings. Notably, CCFS achieves 60.2% test accuracy on ResNet-18 with a 20% compression ratio of Tiny-ImageNet, closely matching full-dataset training with only 0.3% degradation. Code: https://github.com/CYDaaa30/CCFS. | [
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] |
87 | BADGR: Bundle Adjustment Diffusion Conditioned by Gradients for Wide-Baseline Floor Plan Reconstruction | [
"Yuguang Li",
"Ivaylo Boyadzhiev",
"Zixuan Liu",
"Linda Shapiro",
"Alex Colburn"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Li_BADGR_Bundle_Adjustment_Diffusion_Conditioned_by_Gradients_for_Wide-Baseline_Floor_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Li_BADGR_Bundle_Adjustment_Diffusion_Conditioned_by_Gradients_for_Wide-Baseline_Floor_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_BADGR_Bundle_Adjustment_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.19340 | @InProceedings{Li_2025_CVPR,
author = {Li, Yuguang and Boyadzhiev, Ivaylo and Liu, Zixuan and Shapiro, Linda and Colburn, Alex},
title = {BADGR: Bundle Adjustment Diffusion Conditioned by Gradients for Wide-Baseline Floor Plan Reconstruction},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {16785-16795}
} | Reconstructing precise camera poses and floor plan layouts from wide-baseline RGB panoramas is a difficult and unsolved problem. We introduce BADGR, a novel diffusion model that jointly performs reconstruction and bundle adjustment (BA) to refine poses and layouts from a coarse state, using 1D floor boundary predictions from dozens of sparsely captured images. Unlike guided diffusion models, BADGR is conditioned on dense per-column outputs from a single-step Levenberg Marquardt (LM) optimizer and is trained to predict camera and wall positions, while minimizing reprojection errors for view consistency. The objective of layout generation from denoising diffusion process complements BA optimization by providing additional learned layout-structural constraints on top of the co-visible features across images. These constraints help BADGR make plausible guesses about spatial relationships, which constrain the pose graph, such as wall adjacency and collinearity, while also learning to mitigate errors from dense boundary observations using global context. BADGR trains exclusively on 2D floor plans, simplifying data acquisition, enabling robust augmentation, and supporting a variety of input densities. Our experiments validate our method, which significantly outperforms the state-of-the-art pose and floor plan layout reconstruction with different input densities. | [
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88 | Three Cars Approaching within 100m! Enhancing Distant Geometry by Tri-Axis Voxel Scanning for Camera-based Semantic Scene Completion | [
"Jongseong Bae",
"Junwoo Ha",
"Ha Young Kim"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Bae_Three_Cars_Approaching_within_100m_Enhancing_Distant_Geometry_by_Tri-Axis_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Bae_Three_Cars_Approaching_within_100m_Enhancing_Distant_Geometry_by_Tri-Axis_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bae_Three_Cars_Approaching_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2411.16129 | @InProceedings{Bae_2025_CVPR,
author = {Bae, Jongseong and Ha, Junwoo and Kim, Ha Young},
title = {Three Cars Approaching within 100m! Enhancing Distant Geometry by Tri-Axis Voxel Scanning for Camera-based Semantic Scene Completion},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {11939-11948}
} | Camera-based Semantic Scene Completion (SSC) is gaining attentions in the 3D perception field. However, properties such as perspective and occlusion lead to the underestimation of the geometry in distant regions, posing a critical issue for safety-focused autonomous driving systems. To tackle this, we propose ScanSSC, a novel camera-based SSC model composed of a Scan Module and Scan Loss, both designed to enhance distant scenes by leveraging context from near-viewpoint scenes. The Scan Module uses axis-wise masked attention, where each axis employing a near-to-far cascade masking that enables distant voxels to capture relationships with preceding voxels. In addition, the Scan Loss computes the cross-entropy along each axis between cumulative logits and corresponding class distributions in a near-to-far direction, thereby propagating rich context-aware signals to distant voxels. Leveraging the synergy between these components, ScanSSC achieves state-of-the-art performance, with IoUs of 44.54 and 48.29, and mIoUs of 17.40 and 20.14 on the SemanticKITTI and SSCBench-KITTI-360 benchmarks. | [
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] |
89 | MetaShadow: Object-Centered Shadow Detection, Removal, and Synthesis | [
"Tianyu Wang",
"Jianming Zhang",
"Haitian Zheng",
"Zhihong Ding",
"Scott Cohen",
"Zhe Lin",
"Wei Xiong",
"Chi-Wing Fu",
"Luis Figueroa",
"Soo Ye Kim"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Wang_MetaShadow_Object-Centered_Shadow_Detection_Removal_and_Synthesis_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_MetaShadow_Object-Centered_Shadow_Detection_Removal_and_Synthesis_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_MetaShadow_Object-Centered_Shadow_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2412.02635 | @InProceedings{Wang_2025_CVPR,
author = {Wang, Tianyu and Zhang, Jianming and Zheng, Haitian and Ding, Zhihong and Cohen, Scott and Lin, Zhe and Xiong, Wei and Fu, Chi-Wing and Figueroa, Luis and Kim, Soo Ye},
title = {MetaShadow: Object-Centered Shadow Detection, Removal, and Synthesis},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {28252-28262}
} | Shadows are often underconsidered or even ignored in image editing applications, limiting the realism of the edited results. In this paper, we introduce MetaShadow, a three-in-one versatile framework that enables detection, removal, and controllable synthesis of shadows in natural images in an object-centered fashion. MetaShadow combines the strengths of two cooperative components: Shadow Analyzer, for object-centered shadow detection and removal, and Shadow Synthesizer, for reference-based controllable shadow synthesis. Notably, we optimize the learning of the intermediate features from Shadow Analyzer to guide Shadow Synthesizer to generate more realistic shadows that blend seamlessly with the scene. Extensive evaluations on multiple shadow benchmark datasets show significant improvements of MetaShadow over the existing state-of-the-art methods on object-centered shadow detection, removal, and synthesis. MetaShadow excels in supporting imageediting tasks such as object removal, relocation, and insertion, pushing the boundaries of object-centered image editing. | [
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] |
90 | TANGO: Training-free Embodied AI Agents for Open-world Tasks | [
"Filippo Ziliotto",
"Tommaso Campari",
"Luciano Serafini",
"Lamberto Ballan"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Ziliotto_TANGO_Training-free_Embodied_AI_Agents_for_Open-world_Tasks_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Ziliotto_TANGO_Training-free_Embodied_AI_Agents_for_Open-world_Tasks_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ziliotto_TANGO_Training-free_Embodied_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2412.10402 | @InProceedings{Ziliotto_2025_CVPR,
author = {Ziliotto, Filippo and Campari, Tommaso and Serafini, Luciano and Ballan, Lamberto},
title = {TANGO: Training-free Embodied AI Agents for Open-world Tasks},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {24603-24613}
} | Large Language Models (LLMs) have demonstrated excellent capabilities in composing various modules together to create programs that can perform complex reasoning tasks on images. In this paper, we propose TANGO, an approach that extends the program composition via LLMs already observed for images, aiming to integrate those capabilities into embodied agents capable of observing and acting in the world. Specifically, by employing a simple PointGoal Navigation model combined with a memory-based exploration policy as a foundational primitive for guiding an agent through the world, we show how a single model can address diverse tasks without additional training. We task an LLM with composing the provided primitives to solve a specific task, using only a few in-context examples in the prompt. We evaluate our approach on three key Embodied AI tasks: Open-Set ObjectGoal Navigation, Multi-Modal Lifelong Navigation, and Open Embodied Question Answering, achieving state-of-the-art results without any specific fine-tuning in challenging zero-shot scenarios. | [
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91 | SATA: Spatial Autocorrelation Token Analysis for Enhancing the Robustness of Vision Transformers | [
"Nick Nikzad",
"Yi Liao",
"Yongsheng Gao",
"Jun Zhou"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Nikzad_SATA_Spatial_Autocorrelation_Token_Analysis_for_Enhancing_the_Robustness_of_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Nikzad_SATA_Spatial_Autocorrelation_Token_Analysis_for_Enhancing_the_Robustness_of_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Nikzad_SATA_Spatial_Autocorrelation_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2409.19850 | @InProceedings{Nikzad_2025_CVPR,
author = {Nikzad, Nick and Liao, Yi and Gao, Yongsheng and Zhou, Jun},
title = {SATA: Spatial Autocorrelation Token Analysis for Enhancing the Robustness of Vision Transformers},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {9730-9739}
} | Over the past few years, vision transformers (ViTs) have consistently demonstrated remarkable performance across various visual recognition tasks. However, attempts to enhance their robustness have yielded limited success, mainly focusing on different training strategies, input patch augmentation, or network structural enhancements. These approaches often involve extensive training and fine-tuning, which are time-consuming and resource-intensive. To tackle these obstacles, we introduce a novel approach named Spatial Autocorrelation Token Analysis (SATA). By harnessing spatial relationships between token features, SATA enhances both the representational capacity and robustness of ViT models. This is achieved through the analysis and grouping of tokens according to their spatial autocorrelation scores prior to their input into the Feed-Forward Network (FFN) block of the self-attention mechanism. Importantly, SATA seamlessly integrates into existing pre-trained ViT baselines without requiring retraining or additional fine-tuning, while concurrently improving efficiency by reducing the computational load of the FFN units. Experimental results show that the baseline ViTs enhanced with SATA not only achieve a new state-of-the-art top-1 accuracy on ImageNet-1K image classification (94.9%) but also establish new state-of-the-art performance across multiple robustness benchmarks, including ImageNet-A (top-1=63.6%), ImageNet-R (top-1=79.2%), and ImageNet-C (mCE=13.6%), all without requiring additional training or fine-tuning of baseline models. Availability: https://github.com/nick-nikzad/SATA | [
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92 | DViN: Dynamic Visual Routing Network for Weakly Supervised Referring Expression Comprehension | [
"Xiaofu Chen",
"Yaxin Luo",
"Gen Luo",
"Jiayi Ji",
"Henghui Ding",
"Yiyi Zhou"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Chen_DViN_Dynamic_Visual_Routing_Network_for_Weakly_Supervised_Referring_Expression_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_DViN_Dynamic_Visual_Routing_Network_for_Weakly_Supervised_Referring_Expression_CVPR_2025_paper.pdf | null | null | @InProceedings{Chen_2025_CVPR,
author = {Chen, Xiaofu and Luo, Yaxin and Luo, Gen and Ji, Jiayi and Ding, Henghui and Zhou, Yiyi},
title = {DViN: Dynamic Visual Routing Network for Weakly Supervised Referring Expression Comprehension},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {14347-14357}
} | In this paper, we focus on weakly supervised referring expression comprehension (REC), and identify that the lack of fine-grained visual capability greatly limits the upper performance bound of existing methods. To address this issue, we propose a novel framework for weakly supervised REC, namely Dynamic Visual routing Network (DViN), which overcomes the visual shortcomings from the perspective of feature combination and alignment. In particular, DViN is equipped with a novel sparse routing mechanism to efficiently combine features of multiple visual encoders in a dynamic manner, thus improving the visual descriptive power. Besides, we further propose an innovative weakly supervised objective, namely Routing-based Feature Alignment (RFA), which facilitates the visual understanding of routed features through the intra-modal and inter-modal alignment. To validate DViN, we conduct extensive experiments on four REC benchmark datasets. Experiments demonstrate that DViN achieves state-of-the-art results on four benchmarks while maintaining competitive inference efficiency. Besides, the strong generalization ability of DViN is also validated on weakly supervised referring expression segmentation. Source codes are anonymously released at: https://anonymous.4open.science/r/DViN-7736. | [
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] |
93 | Nested Diffusion Models Using Hierarchical Latent Priors | [
"Xiao Zhang",
"Ruoxi Jiang",
"Rebecca Willett",
"Michael Maire"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Nested_Diffusion_Models_Using_Hierarchical_Latent_Priors_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Nested_Diffusion_Models_Using_Hierarchical_Latent_Priors_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Nested_Diffusion_Models_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2412.05984 | @InProceedings{Zhang_2025_CVPR,
author = {Zhang, Xiao and Jiang, Ruoxi and Willett, Rebecca and Maire, Michael},
title = {Nested Diffusion Models Using Hierarchical Latent Priors},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {2502-2512}
} | We introduce nested diffusion models, an efficient and powerful hierarchical generative framework that substantially enhances the generation quality of diffusion models, particularly for images of complex scenes. Our approach employs a series of diffusion models to progressively generate latent variables at different semantic levels. Each model in this series is conditioned on the output of the preceding higher-level models, culminating in image generation. Hierarchical latent variables guide the generation process along predefined semantic pathways, allowing our approach to capture intricate structural details. To construct these latent variables, we leverage a pre-trained visual encoder, which learns strong semantic visual representations, and modulate its capacity via dimensionality reduction and noise injection. Across multiple datasets, our system demonstrates significant enhancements in image quality for both unconditional and class/text conditional generation. Moreover, our unconditional generation system substantially outperforms the baseline conditional system. These advancements incur minimal computational overhead as the more abstract levels of our hierarchy work with lower-dimensional representations. | [
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] |
94 | A Theory of Learning Unified Model via Knowledge Integration from Label Space Varying Domains | [
"Dexuan Zhang",
"Thomas Westfechtel",
"Tatsuya Harada"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_A_Theory_of_Learning_Unified_Model_via_Knowledge_Integration_from_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_A_Theory_of_Learning_Unified_Model_via_Knowledge_Integration_from_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_A_Theory_of_CVPR_2025_supplemental.pdf | null | @InProceedings{Zhang_2025_CVPR,
author = {Zhang, Dexuan and Westfechtel, Thomas and Harada, Tatsuya},
title = {A Theory of Learning Unified Model via Knowledge Integration from Label Space Varying Domains},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {10142-10152}
} | Existing domain adaptation systems can hardly be applied to real-world problems with new classes presenting at deployment time, especially regarding source-free scenarios where multiple source domains do not share the label space despite being given a few labeled target data. To address this, we consider a challenging problem: multi-source semi-supervised open-set domain adaptation and propose a learning theory via joint error, effectively tackling strong domain shift. To generalize the algorithm into source-free cases, we introdcue a computationally efficient and architecture-flexible attention-based feature generation module. Extensive experiments on various data sets demonstrate the significant improvement of our proposed algorithm over baselines. | [
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95 | HiLoTs: High-Low Temporal Sensitive Representation Learning for Semi-Supervised LiDAR Segmentation in Autonomous Driving | [
"R.D. Lin",
"Pengcheng Weng",
"Yinqiao Wang",
"Han Ding",
"Jinsong Han",
"Fei Wang"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Lin_HiLoTs_High-Low_Temporal_Sensitive_Representation_Learning_for_Semi-Supervised_LiDAR_Segmentation_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Lin_HiLoTs_High-Low_Temporal_Sensitive_Representation_Learning_for_Semi-Supervised_LiDAR_Segmentation_CVPR_2025_paper.pdf | null | http://arxiv.org/abs/2503.17752 | @InProceedings{Lin_2025_CVPR,
author = {Lin, R.D. and Weng, Pengcheng and Wang, Yinqiao and Ding, Han and Han, Jinsong and Wang, Fei},
title = {HiLoTs: High-Low Temporal Sensitive Representation Learning for Semi-Supervised LiDAR Segmentation in Autonomous Driving},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {1429-1438}
} | LiDAR point cloud semantic segmentation plays a crucial role in autonomous driving. In recent years, semi-supervised methods have gained popularity due to their significant reduction in annotation labor and time costs. Current semi-supervised methods typically focus on point cloud spatial distribution or consider short-term temporal representations, e.g., only two adjacent frames, often overlooking the rich long-term temporal properties inherent in autonomous driving scenarios. In driving experience, we observe that nearby objects, such as roads and vehicles, remain stable while driving, whereas distant objects exhibit greater variability in category and shape. This natural phenomenon is also captured by LiDAR, which reflects lower temporal sensitivity for nearby objects and higher sensitivity for distant ones. To leverage these characteristics, we propose HiLoTs, which learns high-temporal sensitivity and low-temporal sensitivity representations from continuous LiDAR frames. These representations are further enhanced and fused using a cross-attention mechanism. Additionally, we employ a teacher-student framework to align the representations learned by the labeled and unlabeled branches, effectively utilizing the large amounts of unlabeled data. Experimental results on the SemanticKITTI and nuScenes datasets demonstrate that our proposed HiLoTs outperforms state-of-the-art semi-supervised methods, and achieves performance close to LiDAR+Camera multimodal approaches. | [
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] |
96 | Spiking Transformer with Spatial-Temporal Attention | [
"Donghyun Lee",
"Yuhang Li",
"Youngeun Kim",
"Shiting Xiao",
"Priyadarshini Panda"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Lee_Spiking_Transformer_with_Spatial-Temporal_Attention_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_Spiking_Transformer_with_Spatial-Temporal_Attention_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lee_Spiking_Transformer_with_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2409.19764 | @InProceedings{Lee_2025_CVPR,
author = {Lee, Donghyun and Li, Yuhang and Kim, Youngeun and Xiao, Shiting and Panda, Priyadarshini},
title = {Spiking Transformer with Spatial-Temporal Attention},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {13948-13958}
} | Spike-based Transformer presents a compelling and energy-efficient alternative to traditional Artificial Neural Network (ANN)-based Transformers, achieving impressive results through sparse binary computations. However, existing spike-based transformers predominantly focus on spatial attention while neglecting crucial temporal dependencies inherent in spike-based processing, leading to suboptimal feature representation and limited performance. To address this limitation, we propose Spiking Transformer with Spatial-Temporal Attention (STAtten), a simple and straightforward architecture that efficiently integrates both spatial and temporal information in the self-attention mechanism. STAtten introduces a block-wise computation strategy that processes information in spatial-temporal chunks, enabling comprehensive feature capture while maintaining the same computational complexity as previous spatial-only approaches. Our method can be seamlessly integrated into existing spike-based transformers without architectural overhaul. Extensive experiments demonstrate that STAtten significantly improves the performance of existing spike-based transformers across both static and neuromorphic datasets, including CIFAR10/100, ImageNet, CIFAR10-DVS, and N-Caltech101. | [
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] |
97 | Perceptual Video Compression with Neural Wrapping | [
"Muhammad Umar Karim Khan",
"Aaron Chadha",
"Mohammad Ashraful Anam",
"Yiannis Andreopoulos"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Khan_Perceptual_Video_Compression_with_Neural_Wrapping_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Khan_Perceptual_Video_Compression_with_Neural_Wrapping_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Khan_Perceptual_Video_Compression_CVPR_2025_supplemental.pdf | null | @InProceedings{Khan_2025_CVPR,
author = {Khan, Muhammad Umar Karim and Chadha, Aaron and Anam, Mohammad Ashraful and Andreopoulos, Yiannis},
title = {Perceptual Video Compression with Neural Wrapping},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {17743-17754}
} | Standard video codecs are rate-distortion optimization machines, where distortion is typically quantified using PSNR versus the source. However, it is now widely accepted that increasing PSNR does not necessarily translate to better visual quality. In this paper, a better balance between perception and fidelity is targeted, in order to provide for significant rate savings over state-of-the-art standards-based video codecs. Specifically, pre- and postprocessing neural networks are proposed that enhance the coding efficiency of standard video codecs when benchmarked with an array of well-established perceptual quality scores. These "neural wrapper" elements are end-to-end trained with a neural codec module serving as a differentiable proxy for standard video codecs. The codec proxy is jointly optimized with the pre- and post components, via a novel two-phase pretraining strategy and end-to-end iterative refinement with stop-gradient. This allows the neural pre- and postprocessor to learn to embed, remove and recover information in a codec-aware manner, thus improving its rate-quality performance. A single neural-wrapper model is thereby established and used for the entire rate-quality curve without needing any downscaling or upscaling. The trained model is tested with the AV1 and VVC standard codecs via an array of well-established objective quality scores (SSIM, MS-SSIM, VMAF, AVQT), as well as mean opinion scores (MOS) derived from ITU-T P.910 subjective testing. Experimental results show that the proposed approach improves all quality scores, with -18.5% average Bjontegaard Delta-rate (BD-rate) saving over all objective scores and MOS improvement over both standard codecs. This illustrates the significant potential of neural wrapper components over standards-based video coding. | [
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98 | ViKIENet: Towards Efficient 3D Object Detection with Virtual Key Instance Enhanced Network | [
"Zhuochen Yu",
"Bijie Qiu",
"Andy W. H. Khong"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Yu_ViKIENet_Towards_Efficient_3D_Object_Detection_with_Virtual_Key_Instance_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_ViKIENet_Towards_Efficient_3D_Object_Detection_with_Virtual_Key_Instance_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yu_ViKIENet_Towards_Efficient_CVPR_2025_supplemental.pdf | null | @InProceedings{Yu_2025_CVPR,
author = {Yu, Zhuochen and Qiu, Bijie and Khong, Andy W. H.},
title = {ViKIENet: Towards Efficient 3D Object Detection with Virtual Key Instance Enhanced Network},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {11844-11853}
} | The sparsity of point clouds and inadequacy of semantic information pose challenges to current LiDAR-only 3D object detection methods. Recent methods alleviate these challenges by converting RGB images into virtual points via depth completion to be fused with LiDAR points. Although these methods have shown outstanding results, they often introduce significant computation overhead due to the high density of virtual points and noise due to inaccurate depth completion. Besides, they do not thoroughly leverage semantic information from images. In this work, we propose the virtual key instance enhanced network (ViKIENet), a highly efficient and effective multi-modal feature fusion framework that fuses the features of virtual key instances (VKIs) and LiDAR points through multiple stages. Our contributions include three main components: semantic key instance selection (SKIS), virtual-instance-focused fusion (VIFF), and virtual-instance-to-real attention (VIRA). We also propose the extended version ViKIENet-R with VIFF-R which includes rotationally equivariant features. Experiment results show that ViKIENet and ViKIENet-R achieve significant improvements in detection performance on the KITTI, JRDB, and nuScenes datasets compared to existing works. On the KITTI dataset, ViKIENet and ViKIENet-R operate at 22.7 and 15.0 FPS, respectively. As of CVPR submission (Nov. 15th, 2024), ViKIENet ranks first on the car detection and orientation estimation leaderboard, while ViKIENet-R ranks second (compared with officially published papers) on the 3D car detection leaderboard. | [
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99 | DKDM: Data-Free Knowledge Distillation for Diffusion Models with Any Architecture | [
"Qianlong Xiang",
"Miao Zhang",
"Yuzhang Shang",
"Jianlong Wu",
"Yan Yan",
"Liqiang Nie"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Xiang_DKDM_Data-Free_Knowledge_Distillation_for_Diffusion_Models_with_Any_Architecture_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Xiang_DKDM_Data-Free_Knowledge_Distillation_for_Diffusion_Models_with_Any_Architecture_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xiang_DKDM_Data-Free_Knowledge_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2409.03550 | @InProceedings{Xiang_2025_CVPR,
author = {Xiang, Qianlong and Zhang, Miao and Shang, Yuzhang and Wu, Jianlong and Yan, Yan and Nie, Liqiang},
title = {DKDM: Data-Free Knowledge Distillation for Diffusion Models with Any Architecture},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {2955-2965}
} | Diffusion models (DMs) have demonstrated exceptional generative capabilities across various domains, including image, video, and so on. A key factor contributing to their effectiveness is the high quantity and quality of data used during training. However, mainstream DMs now consume increasingly large amounts of data. For example, training a Stable Diffusion model requires billions of image-text pairs. This enormous data requirement poses significant challenges for training large DMs due to high data acquisition costs and storage expenses. To alleviate this data burden, we propose a novel scenario: using existing DMs as data sources to train new DMs with any architecture. We refer to this scenario as Data-Free Knowledge Distillation for Diffusion Models (DKDM), where the generative ability of DMs is transferred to new ones in a data-free manner. To tackle this challenge, we make two main contributions. First, we introduce a DKDM objective that enables the training of new DMs via distillation, without requiring access to the data. Second, we develop a dynamic iterative distillation method that efficiently extracts time-domain knowledge from existing DMs, enabling direct retrieval of training data without the need for a prolonged generative process. To the best of our knowledge, we are the first to explore this scenario. Experimental results demonstrate that our data-free approach not only achieves competitive generative performance but also, in some instances, outperforms models trained with the entire dataset. | [
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