| --- |
| license: mit |
| task_categories: |
| - text-to-3d |
| - image-to-3d |
| tags: |
| - 3d-editing |
| - shape-editing |
| - shape-generation |
| - evaluation-benchmark |
| - siggraph2026 |
| size_categories: |
| - n<1K |
| --- |
| |
| # Prox-E ShapeTalk Evaluation Benchmark Dataset |
|
|
| This is the official benchmark dataset for **Prox-E: Fine-Grained 3D Shape Editing via Primitive-Based Abstractions (SIGGRAPH'26)**. |
|
|
| ## 📄 Dataset Overview |
| This benchmark consists of a subset of random **600 samples** from the **ShapeTalk** dataset. It is used to evaluate identity preservation, 3D quality, and edit fidelity. |
|
|
| ## 📂 Repository Structure |
| The repository is structured to seamlessly plug into the `Prox-E` unified evaluator: |
|
|
| - **`input_shapes/`**: Directory containing `600` input 3D meshes in OBJ format (`.obj`). |
| - **`rendered_images/`**: 600 high-resolution upright 2D renders (PNG) of the input shapes, produced from TRELLIS's inversion outputs. |
| - **`point_cloud/`**: Directory containing `600` dense point cloud of the input meshes (`.npz`). |
| - **`instructions.json`**: Key-value metadata containing evaluation prompts, object classes and part-specific keywords. |
| - **`shapetalk_600_set.csv`**: Full detailed metadata from the original ShapeTalk dataset on this 600-sample subset. |
| |
| --- |
| |
| ## 🛠️ Usage with Prox-E Evaluation Tool |
| |
| ### 1. Download the Dataset |
| You can clone this dataset repository using Git LFS: |
| ```bash |
| git lfs install |
| git clone https://huggingface.co/datasets/haopt/prox-e-shapetalk-benchmark |
| ``` |
| |
| ### 2. Run the Unified Evaluator |
| Once downloaded, plug this benchmark directly into the Prox-E unified evaluation pipeline to compute metric scores (e.g., l-GD, LPIPS, DINO-I, FID, PFD, CLIP, and VQA): |
| |
| ```bash |
| python -m evals.main \ |
| --pred_dir <flat folder of pred .glb/.obj/.ply> \ |
| --input_dir prox-e-shapetalk-benchmark/input_shapes \ |
| --instructions_json prox-e-shapetalk-benchmark/instructions.json \ |
| --input_render_dir prox-e-shapetalk-benchmark/rendered_images \ |
| --input_pcd_dir prox-e-shapetalk-benchmark/point_cloud \ |
| --output_dir <eval_run_dir> \ |
| --device cuda:0 \ |
| --metrics identity quality fidelity \ |
| --enable_vqa |
| ``` |
| |
| Refer to the official [Prox-E repo](https://github.com/etaisella/Prox-E/tree/main/evals) for setup and full usage options! |
| |
| ## 📜 Citation |
| If you use this benchmark dataset in your work, please cite the Prox-E paper and the ShapeTalk dataset: |
| |
| ```bibtex |
| @inproceedings{sella2026proxefinegrained3dshape, |
| title={Prox-E: Fine-Grained 3D Shape Editing via Primitive-Based Abstractions}, |
| author={Etai Sella and Hao Phung and Nitay Amiel and Or Litany and Or Patashnik and Hadar Averbuch-Elor}, |
| booktitle={Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers}, |
| year={2026}, |
| } |
| |
| @inproceedings{achlioptas2023shapetalk, |
| title={{ShapeTalk}: A Language Dataset and Framework for 3D Shape Edits and Deformations}, |
| author={Achlioptas, Panos and Huang, Ian and Sung, Minhyuk and Tulyakov, Sergey and Guibas, Leonidas}, |
| booktitle={Conference on Computer Vision and Pattern Recognition (CVPR)}, |
| year={2023} |
| } |
| ``` |
