| | --- |
| | license: cc-by-nd-4.0 |
| | task_categories: |
| | - text-to-3d |
| | tags: |
| | - 3d |
| | - benchmark |
| | - out-of-domain |
| | - evaluation |
| | --- |
| | |
| | # 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](https://huggingface.co/papers/2508.16577). |
| |
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| | 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](https://huggingface.co/papers/2508.16577) |
| | * **Project Page:** https://yosefdayani.github.io/MV-RAG/ |
| | * **Code:** https://github.com/yosefdayani/MV-RAG |
| |
|
| | ## Paper Abstract |
| |
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| | 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. |
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| |
|
| | ## Citation |
| |
|
| | If you use this benchmark or the MV-RAG model in your research, please cite: |
| |
|
| | ``` bibtex |
| | @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}, |
| | } |
| | ``` |
