Fariborz Teherkhani, Aashish Rai*, Shaunak Srivastava*, Quankai Gao*, Xuanbai Chen, Fernando de la Torre, Steven Song, Aayush Prakash, Daeil Kim (* equal contribution)
This is the official Pytorch implementation of the paper.
[Project Page] [Video] [Colab Demo] [Arxiv]
Download pre-trained weights and put the "checkpoints" folder in the main directory. [Link]
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Generate 3D Faces (mesh and texture)
python generate_faces.py
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Generate meshes only
python test_gan3d.py
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Generate textures only
python test_texture.py
- Download Facescape dataset and specify path to the "facescape_trainset" folder.
python preprocess_traindata.py
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Shape
Train AE python train_ae.py
Generate Reduced Data python gen_reduced_data.py
Train GAN python train_gan3d.py
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Texture
Train P-GAN python train_texture.py --init_step 1 --batch_size 128
The code is available under X11 License. Please read the license terms available at [Link]. Quick summary available at [Link].
If you use find this paper/code useful, please consider citing:
@article{taherkhani2022controllable,
title={Controllable 3D Generative Adversarial Face Model via Disentangling Shape and Appearance},
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},
journal={arXiv preprint arXiv:2208.14263},
year={2022}
}