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.

Installation

We rely on torch with CUDA installed correctly (tested with torch 2.5.1 + CUDA 12.1).

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

Windows Installation

It is confirmed to work on Python 3.10, with Cuda 12.4 and Torch 2.51 with TorchVision 0.20.1.

It may work with other versions or combinations, but has been tested and confirm to work on NVidia 3090 and 4090 GPUs.

• Install Python 3.10
• Install Cuda 12.4
• Git Clone the repository
  • git clone https://github.com/NVlabs/PartPacker
• Create a virtual environment inside the PartPacker directory
• Activate the virtual environment
• Install torch for your cuda version (12.4)
  • pip install torch==2.5.1 torchvision==0.20.1 torchaudio --index-url https://download.pytorch.org/whl/cu124
• Install requirements
  • pip install -r requirements.txt

Running the GUI

Run the app with py app.py

It will auto-download the needed models and give you a URL for the gradio app in the console.

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 ~10GB 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 (single GPU)
python app.py

# open local gradio app with multi-GPU support
python app.py --multi

Multi-GPU Support

The application supports multi-GPU inference for those who are lack of GPU memory.

• Single GPU mode (default): python app.py
• Multi-GPU mode: python app.py --multi

In multi-GPU mode:

• The flow model is placed on GPU 0
• The VAE model is placed on GPU 1 (if available)
• Automatic memory management and data transfer between GPUs
• Reduced memory pressure per GPU
• Better performance with 2 or more GPUs

If only one GPU is available, the system automatically falls back to single-GPU behavior even in multi-GPU mode.

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!

• Dora
• Hunyuan3D-2
• Trellis

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}
}