PrimitiveAnything: Human-Crafted 3D Primitive Assembly Generation with Auto-Regressive Transformer

🔥 Updates

[2025/05/07] test dataset, code, pretrained checkpoints and Gradio demo are released!

🔍 Table of Contents

⚙️ Deployment
🖥️ Run PrimitiveAnything
📝 Citation

⚙️ Deployment

Set up a Python environment and install the required packages:

conda create -n primitiveanything python=3.9 -y
conda activate primitiveanything

# Install torch, torchvision based on your machine configuration
pip install torch==2.1.0 torchvision==0.16.0 --index-url https://download.pytorch.org/whl/cu118

# Install other dependencies
pip install -r requirements.txt

Then download data and pretrained weights:

Our Model Weights: Download from our 🤗 Hugging Face repository (download here) and place them in ./ckpt/.
Michelangelo’s Point Cloud Encoder: Download weights from Michelangelo’s Hugging Face repo and save them to ./ckpt/.
Demo and test data:

Download from this Google Drive link, then decompress the files into ./data/.

After downloading and organizing the files, your project directory should look like this:

- data/
    ├── basic_shapes_norm/
    ├── basic_shapes_norm_pc10000/
    ├── demo_glb/                   # Demo files in GLB format
    └── test_pc/                    # Test point cloud data
- ckpt/
    ├── mesh-transformer.ckpt.60.pt # Our model checkpoint
    └── shapevae-256.ckpt           # Michelangelo ShapeVAE checkpoint

🖥️ Run PrimitiveAnything

Demo

python demo.py --input ./data/demo_glb --log_path ./results/demo

Notes:

--input accepts either:
- Any standard 3D file (GLB, OBJ, etc.)
- A directory containing multiple 3D files
For optimal results with fine structures, we automatically apply marching cubes and dilation operations (which differs from testing and evaluation). This prevents quality degradation in thin areas.

Testing and Evaluation

# Autoregressive generation
python infer.py

# Sample point clouds from predictions  
python sample.py

# Calculate evaluation metrics
python eval.py

📝 Citation

If you find our work useful, please kindly cite:

@article{ye2025primitiveanything,
  title={PrimitiveAnything: Human-Crafted 3D Primitive Assembly Generation with Auto-Regressive Transformer},
  author={Ye, Jingwen and He, Yuze and Zhou, Yanning and Zhu, Yiqin and Xiao, Kaiwen and Liu, Yong-Jin and Yang, Wei and Han, Xiao},
  journal={arXiv preprint arXiv:250x.xxxxx},
  year={2025}
}

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