Enhance OOD-Eval dataset card: Add task category, links, abstract, and usage example

README.md

@@ -1,3 +1,59 @@
----
-license: cc-by-nd-4.0
----
+---
+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).
+
+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
+
+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.
+
+## Sample Usage
+
+You can prompt the MV-RAG model (which leverages this dataset for evaluation) on your retrieved local images by:
+
+```bash
+python main.py \
+--prompt "Cadillac 341 automobile car" \
+--retriever simple \
+--folder_path "assets/Cadillac 341 automobile car" \
+--seed 0 \
+--k 4 \
+--azimuth_start 45  # or 0 for front view
+```
+
+To see all command options run
+```bash
+python main.py --help
+```
+
+## 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}, 
+}
+```