haopt/prox-e-shapetalk-benchmark

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.

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🛠️ Usage with Prox-E Evaluation Tool

1. Download the Dataset

You can clone this dataset repository using Git LFS:

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):

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 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:

@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}
}