PartCrafter

[NeurIPS 2025] PartCrafter: Structured 3D Mesh Generation via Compositional Latent Diffusion Transformers

Yuchen Lin*, Chenguo Lin*, Panwang Pan†, Honglei Yan, Yiqiang Feng, Yadong Mu, Katerina Fragkiadaki

This repository contains the official implementation of the paper: PartCrafter: Structured 3D Mesh Generation via Compositional Latent Diffusion Transformers. 
PartCrafter is a structured 3D generative model that jointly generates multiple parts and objects from a single RGB image in one shot. 
Here is our Project Page.

Feel free to contact me (linyuchen@stu.pku.edu.cn) or open an issue if you have any questions or suggestions.

📢 News
- 2025-09-18: PartCrafter is accepted to NeurIPS 2025. 
- 2025-08-15: PartCrafter HuggingFace🤗 demo is available here. Thanks to alexnasa. 
- 2025-07-23: The 3D scene version of PartCrafter is released, which is trained on 3D-Front. 
- 2025-07-20: A guide for installing PartCrafter on Windows is available in this fork. Thanks to JackDainzh!
- 2025-07-13: PartCrafter is fully open-sourced 🚀.
- 2025-06-09: PartCrafter is on arXiv. 

📋 TODO
- [x] Release inference scripts. 
- [x] Release training code and data preprocessing scripts. 
- [x] Release pretrained checkpoints on both object and scene level. 
- [x] Provide a HuggingFace🤗 demo.
- [ ] Release preprocessed dataset. 

🔧 Installation
We use torch-2.5.1+cu124 and python-3.11. But it should also work with other versions. Create a conda environment with the following command (optional):

conda create -n partcrafter python=3.11.13
conda activate partcrafter
pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu124

Then, install other dependencies with the following command:

git clone https://github.com/wgsxm/PartCrafter.git
cd PartCrafter
bash settings/setup.sh

If you do not have root access and use conda environment, you can install required graphics libraries with the following command:

conda install -c conda-forge libegl libglu pyopengl

We test the above installation on Debian 12 with NVIDIA H20 GPUs. For Windows users, you can try to set up the environment according to this pull request and this fork. We sincerely thank JackDainzh for contributing to the Windows support! 

💡 Quick Start
3D Part-Level Object Generation

Generate a 3D part-level object from an image:

python scripts/inference_partcrafter.py \
  --image_path assets/images/np3_2f6ab901c5a84ed6bbdf85a67b22a2ee.png \
  --num_parts 3 --tag robot --render

The required model weights will be automatically downloaded:
- PartCrafter model from wgsxm/PartCrafter → pretrained_weights/PartCrafter
- RMBG model from briaai/RMBG-1.4 → pretrained_weights/RMBG-1.4

The generated results will be saved to ./results/robot. We provide several example images from Objaverse and ABO in ./assets/images. Their filenames start with recommended number of parts, e.g., np3 which means 3 parts. You can also try other part count for the same input images. 

Specify --rmbg if you use custom images. This will remove the background of the input image and resize it appropriately.

VLM-Based Part Suggestion
Instead of manually specifying --num_parts, you can

……（后略，见原文）