FaceG2E

Paper | Project Page | Arxiv

This repository is the official implementation of our work FaceG2E.

Text-Guided 3D Face Synthesis -- From Generation to Editing
Yunjie Wu, Yapeng Meng, Zhipeng Hu, Lincheng Li, Haoqian Wu, Kun Zhou, Weiwei Xu, Xin Yu
In CVPR2024
FUXI AILab, Netease, Hangzhou, China

Getting Started

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install the environement

conda env create -f faceg2e.yaml
conda activate faceg2e
bash install_extra_lib.sh

This implementation is only tested under the device:

• System: Unbuntu 18.04
• GPU: A30
• Cuda Version: 12.0
• Cuda Driver Version: 525.78.01

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prepare the data and ckpts

Download our pretrained texture diffusion ckpts and put them in ./ckpts directory.

Download the HIFI3D++ 3DMM files (AI-NExT-Albedo-Global.mat and HIFI3D++.mat) and put them in ./HIFI3D directory.

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test generation and editing

facial geometry generation

bash demo_geometry_generation.sh

facial texture generation

bash demo_texture_generation.sh

face editing

bash demo_editing.sh

The results are saved in exp/demo.

Note

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1. During editing, you need to input a token indice to indicates the token which determines the consistency-preservation mask. If your editing is a global effect to the face, you can input 0 as indice.

2. The weighting parameters in demo_editing.sh controls editing effect. You can adjust them by yourself. For example, a higher edit_prompt_cfg makes editing more obvious, and a higher w_reg_diffuse makes unrelated regions more consistent.

Results

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Generation of Scarlett Johansson, Cate Blanchett, and Tom Cruise.

Generation of Neteyam in Avatar, Thanos, and Kratos.

Editing of Make his eye mask blue, Make him chubby, and Turn his eyemask golden.

Contact

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If you have any questions, please contact Yunjie Wu (jiejiangwu@outlook.com).

Citation

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If you use our work in your research, please cite our publication:

@inproceedings{wu2024text,
  title={Text-Guided 3D Face Synthesis-From Generation to Editing},
  author={Wu, Yunjie and Meng, Yapeng and Hu, Zhipeng and Li, Lincheng and Wu, Haoqian and Zhou, Kun and Xu, Weiwei and Yu, Xin},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={1260--1269},
  year={2024}
}

Acknowledgements

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There are some functions or scripts in this implementation that are based on external sources. We thank the authors for their excellent works. Here are some great resources we benefit:

• Deep3DFaceRecon_pytorch for the rendering framework code.
• Nvdiffrast for differentiable rendering.
• REALY for 3D Morphable Model.
• Stable-dreamfusion for SDS code.
• BoxDiff for token-image cross-attention compuatation.