Datasets:
image
imagewidth (px) 108
3.26k
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class label 196
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0AJS 500 cc 1954 E95 Porcupine racer motorcycle
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0AJS 500 cc 1954 E95 Porcupine racer motorcycle
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0AJS 500 cc 1954 E95 Porcupine racer motorcycle
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0AJS 500 cc 1954 E95 Porcupine racer motorcycle
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1Aermacchi 350 cc ala dóro motorcycle
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1Aermacchi 350 cc ala dóro motorcycle
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1Aermacchi 350 cc ala dóro motorcycle
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1Aermacchi 350 cc ala dóro motorcycle
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2Afghan Hound dog
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2Afghan Hound dog
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2Afghan Hound dog
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2Afghan Hound dog
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2Afghan Hound dog
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2Afghan Hound dog
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2Afghan Hound dog
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2Afghan Hound dog
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2Afghan Hound dog
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3Airedale Terrier dog
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3Airedale Terrier dog
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3Airedale Terrier dog
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3Airedale Terrier dog
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3Airedale Terrier dog
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3Airedale Terrier dog
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3Airedale Terrier dog
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3Airedale Terrier dog
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3Airedale Terrier dog
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3Airedale Terrier dog
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3Airedale Terrier dog
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3Airedale Terrier dog
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3Airedale Terrier dog
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3Airedale Terrier dog
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4American Bully dog
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4American Bully dog
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4American Bully dog
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4American Bully dog
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4American Bully dog
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4American Bully dog
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4American Bully dog
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4American Bully dog
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4American Bully dog
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4American Bully dog
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4American Bully dog
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4American Bully dog
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4American Bully dog
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4American Bully dog
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4American Bully dog
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4American Bully dog
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4American Bully dog
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4American Bully dog
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4American Bully dog
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4American Bully dog
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4American Bully dog
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4American Bully dog
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5American Cocker Spaniel dog
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5American Cocker Spaniel dog
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5American Cocker Spaniel dog
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5American Cocker Spaniel dog
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5American Cocker Spaniel dog
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5American Cocker Spaniel dog
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5American Cocker Spaniel dog
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5American Cocker Spaniel dog
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5American Cocker Spaniel dog
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6American Eskimo Dog
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6American Eskimo Dog
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6American Eskimo Dog
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6American Eskimo Dog
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6American Eskimo Dog
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6American Eskimo Dog
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6American Eskimo Dog
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6American Eskimo Dog
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7American Hairless Terrier dog
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7American Hairless Terrier dog
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7American Hairless Terrier dog
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7American Hairless Terrier dog
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7American Hairless Terrier dog
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7American Hairless Terrier dog
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8American Water Spaniel dog
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8American Water Spaniel dog
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8American Water Spaniel dog
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8American Water Spaniel dog
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8American Water Spaniel dog
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8American Water Spaniel dog
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8American Water Spaniel dog
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8American Water Spaniel dog
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9Amphicar Model 770
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9Amphicar Model 770
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9Amphicar Model 770
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9Amphicar Model 770
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10Ankara simit dish
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10Ankara simit dish
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10Ankara simit dish
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11Appenzeller Sennenhund dog
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11Appenzeller Sennenhund dog
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11Appenzeller Sennenhund dog
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11Appenzeller Sennenhund dog
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11Appenzeller Sennenhund dog
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11Appenzeller Sennenhund dog
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11Appenzeller Sennenhund dog
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11Appenzeller Sennenhund dog
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11Appenzeller Sennenhund dog
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OOD-Eval: Out-of-Domain Evaluation Prompts for Text-to-3D
This repository contains the OOD-Eval dataset, a new collection of challenging out-of-domain (OOD) prompts specifically designed to facilitate rigorous evaluation of text-to-3D generation models. It was introduced in the paper MV-RAG: Retrieval Augmented Multiview Diffusion.
This dataset helps assess how well text-to-3D approaches perform on rare or novel concepts, addressing a limitation where models often struggle to produce consistent or accurate results for such inputs.
- Paper: MV-RAG: Retrieval Augmented Multiview Diffusion
- Project Page: https://yosefdayani.github.io/MV-RAG/
- Code: https://github.com/yosefdayani/MV-RAG
Paper Abstract
Text-to-3D generation approaches have advanced significantly by leveraging pretrained 2D diffusion priors, producing high-quality and 3D-consistent outputs. However, they often fail to produce out-of-domain (OOD) or rare concepts, yielding inconsistent or inaccurate results. To this end, we propose MV-RAG, a novel text-to-3D pipeline that first retrieves relevant 2D images from a large in-the-wild 2D database and then conditions a multiview diffusion model on these images to synthesize consistent and accurate multiview outputs. Training such a retrieval-conditioned model is achieved via a novel hybrid strategy bridging structured multiview data and diverse 2D image collections. This involves training on multiview data using augmented conditioning views that simulate retrieval variance for view-specific reconstruction, alongside training on sets of retrieved real-world 2D images using a distinctive held-out view prediction objective: the model predicts the held-out view from the other views to infer 3D consistency from 2D data. To facilitate a rigorous OOD evaluation, we introduce a new collection of challenging OOD prompts. Experiments against state-to-the-art text-to-3D, image-to-3D, and personalization baselines show that our approach significantly improves 3D consistency, photorealism, and text adherence for OOD/rare concepts, while maintaining competitive performance on standard benchmarks.
Citation
If you use this benchmark or the MV-RAG model in your research, please cite:
@misc{dayani2025mvragretrievalaugmentedmultiview,
title={MV-RAG: Retrieval Augmented Multiview Diffusion},
author={Yosef Dayani and Omer Benishu and Sagie Benaim},
year={2025},
eprint={2508.16577},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2508.16577},
}
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