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196 classes
0AJS 500 cc 1954 E95 Porcupine racer motorcycle
0AJS 500 cc 1954 E95 Porcupine racer motorcycle
0AJS 500 cc 1954 E95 Porcupine racer motorcycle
0AJS 500 cc 1954 E95 Porcupine racer motorcycle
1Aermacchi 350 cc ala dóro motorcycle
1Aermacchi 350 cc ala dóro motorcycle
1Aermacchi 350 cc ala dóro motorcycle
1Aermacchi 350 cc ala dóro motorcycle
2Afghan Hound dog
2Afghan Hound dog
2Afghan Hound dog
2Afghan Hound dog
2Afghan Hound dog
2Afghan Hound dog
2Afghan Hound dog
2Afghan Hound dog
2Afghan Hound dog
3Airedale Terrier dog
3Airedale Terrier dog
3Airedale Terrier dog
3Airedale Terrier dog
3Airedale Terrier dog
3Airedale Terrier dog
3Airedale Terrier dog
3Airedale Terrier dog
3Airedale Terrier dog
3Airedale Terrier dog
3Airedale Terrier dog
3Airedale Terrier dog
3Airedale Terrier dog
3Airedale Terrier dog
4American Bully dog
4American Bully dog
4American Bully dog
4American Bully dog
4American Bully dog
4American Bully dog
4American Bully dog
4American Bully dog
4American Bully dog
4American Bully dog
4American Bully dog
4American Bully dog
4American Bully dog
4American Bully dog
4American Bully dog
4American Bully dog
4American Bully dog
4American Bully dog
4American Bully dog
4American Bully dog
4American Bully dog
4American Bully dog
5American Cocker Spaniel dog
5American Cocker Spaniel dog
5American Cocker Spaniel dog
5American Cocker Spaniel dog
5American Cocker Spaniel dog
5American Cocker Spaniel dog
5American Cocker Spaniel dog
5American Cocker Spaniel dog
5American Cocker Spaniel dog
6American Eskimo Dog
6American Eskimo Dog
6American Eskimo Dog
6American Eskimo Dog
6American Eskimo Dog
6American Eskimo Dog
6American Eskimo Dog
6American Eskimo Dog
7American Hairless Terrier dog
7American Hairless Terrier dog
7American Hairless Terrier dog
7American Hairless Terrier dog
7American Hairless Terrier dog
7American Hairless Terrier dog
8American Water Spaniel dog
8American Water Spaniel dog
8American Water Spaniel dog
8American Water Spaniel dog
8American Water Spaniel dog
8American Water Spaniel dog
8American Water Spaniel dog
8American Water Spaniel dog
9Amphicar Model 770
9Amphicar Model 770
9Amphicar Model 770
9Amphicar Model 770
10Ankara simit dish
10Ankara simit dish
10Ankara simit dish
11Appenzeller Sennenhund dog
11Appenzeller Sennenhund dog
11Appenzeller Sennenhund dog
11Appenzeller Sennenhund dog
11Appenzeller Sennenhund dog
11Appenzeller Sennenhund dog
11Appenzeller Sennenhund dog
11Appenzeller Sennenhund dog
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 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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