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Controllable 3D Generative Adversarial Face Model via Disentangling Shape and Appearance

Fariborz Teherkhani, Aashish Rai*, Shaunak Srivastava*, Quankai Gao*, Xuanbai Chen, Fernando de la Torre, Steven Song, Aayush Prakash, Daeil Kim (* equal contribution)

Carnegie Mellon University, Facebook/Meta

WACV 2023

This is the official Pytorch implementation of the paper.

[Project Page] [Video] [Colab Demo] [Arxiv]

Testing

Conda environment: Refer environment.yml

Download pre-trained weights and put the "checkpoints" folder in the main directory. [Link]

  • Generate 3D Faces (mesh and texture)

    python generate_faces.py

  • Generate meshes only

    python test_gan3d.py

  • Generate textures only

    python test_texture.py

Train your own model

Dataset

We primarily used the FaceScape dataset. It can be downloaded from [Link]. The dataset is restricted to be used for non-commercial research only. Learn more about Facescape License [Link].

Preprocess data

- Download Facescape dataset and specify path to the "facescape_trainset" folder.

python preprocess_traindata.py

Start training

  • Shape

    Train AE
python train_ae.py 

    Generate Reduced Data
python gen_reduced_data.py 

    Train GAN
python train_gan3d.py

  • Texture

    Train P-GAN
python train_texture.py --init_step 1 --batch_size 128

License

The code is available under X11 License. Please read the license terms available at [Link]. Quick summary available at [Link].

Citation

If you use find this paper/code useful, please consider citing:

@InProceedings{Taherkhani_2023_WACV,
    author    = {Taherkhani, Fariborz and Rai, Aashish and Gao, Quankai and Srivastava, Shaunak and Chen, Xuanbai and de la Torre, Fernando and Song, Steven and Prakash, Aayush and Kim, Daeil},
    title     = {Controllable 3D Generative Adversarial Face Model via Disentangling Shape and Appearance},
    booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
    month     = {January},
    year      = {2023},
    pages     = {826-836}
}