3DStyle-Diffusion-Official

Official codes and datasets for ACM MM23 paper "3DStyle-Diffusion: Pursuing Fine-grained Text-driven 3D Stylization with 2D Diffusion Models"

• More configs.
• Training code and configs.
• Objaverse-3DStyle Datasets. The Objaverse-3DStyle dataset is available at here.

System Requirements

• Python >=3.7
• CUDA 11
• Nvidia GPU with 12 GB ram at least
• Open3d >=0.14.1
• the package of clip (https://github.com/openai/CLIP)

Train

Call the below shell scripts to generate example styles.

# candle 
./shells/candle-golden.sh
# silver ring
./shells/ring.sh
# gold ring
./shells/ring3.sh
# a red rose sitting in a white vase
./shells/rose-in-vase.sh
# red rose with green leaves
./shells/rose.sh

The outputs will be saved to results/

Acknowledgments

The 3DStyle-Diffusion code is heavily based on the TANGO project, and the ControlNet.

Citation

@inproceedings{HaiboYangACMMM2023,
  title={3DStyle-Diffusion: Pursuing Fine-grained Text-driven 3D
Stylization with 2D Diffusion Models},
  author={Haibo Yang and Yang Chen and Yingwei Pan and Ting Yao and Zhineng Chen and Tao Mei},
  booktitle={ACM MM},
  year={2023}
}