README.md

metadata

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.

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}, 
}