PartPacker

Project Page | Arxiv | Models | Demo

This is the official implementation of PartPacker: Efficient Part-level 3D Object Generation via Dual Volume Packing.

Our model performs part-level 3D object generation from single-view images.

Install

We rely on torch with CUDA installed correctly.

pip install -r requirements.txt

# if you prefer fixed version of dependencies:
pip install -r requirements.lock.txt

# by default we use torch's built-in attention, if you want to explicitly use flash-attn:
pip install flash-attn --no-build-isolation

# if you want to run data processing and vae inference, please install meshiki:
pip install meshiki

Pretrained models

Download the pretrained models from huggingface, and put them in the pretrained folder.

mkdir pretrained
cd pretrained
wget https://huggingface.co/nvidia/PartPacker/resolve/main/vae.pt
wget https://huggingface.co/nvidia/PartPacker/resolve/main/flow.pt

Inference

For inference, it takes ~16GB GPU memory (assuming float16).

# vae reconstruction of meshes
PYTHONPATH=. python vae/scripts/infer.py --ckpt_path pretrained/vae.pt --input assets/meshes/ --output_dir output/

# flow 3D generation from images
PYTHONPATH=. python flow/scripts/infer.py --ckpt_path pretrained/flow.pt --input assets/images/ --output_dir output/

# open local gradio app
python app.py

Data Processing

We provide a Dual Volume Packing implementation to process raw glb meshes into two separate meshes as proposed in the paper.

cd data
python bipartite_contraction.py ./example_mesh.glb
# the two separate meshes will be saved in ./output

Acknowledgements

This work is built on many amazing research works and open-source projects, thanks a lot to all the authors for sharing!

Citation

@article{tang2024partpacker,
  title={Efficient Part-level 3D Object Generation via Dual Volume Packing},
  author={Tang, Jiaxiang and Lu, Ruijie and Li, Zhaoshuo and Hao, Zekun and Li, Xuan and Wei, Fangyin and Song, Shuran and Zeng, Gang and Liu, Ming-Yu and Lin, Tsung-Yi},
  journal={arXiv preprint arXiv:2506.09980},
  year={2025}
}