https://github.com/aashishrai3799/3dfacecam

Implementation of a 3D Face Generative Model
https://github.com/aashishrai3799/3dfacecam

3d-face 3d-face-modelling auto-encoder generative-model mesh-generation shape-synthesis texture-synthesis

Last synced: about 1 month ago
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Implementation of a 3D Face Generative Model

• Host: GitHub
• URL: https://github.com/aashishrai3799/3dfacecam
• Owner: aashishrai3799
• License: other
• Created: 2022-06-21T21:04:11.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-12-29T20:04:51.000Z (about 2 years ago)
• Last Synced: 2023-05-19T05:15:33.762Z (over 1 year ago)
• Topics: 3d-face, 3d-face-modelling, auto-encoder, generative-model, mesh-generation, shape-synthesis, texture-synthesis
• Language: Python
• Homepage:
• Size: 59.9 MB
• Stars: 66
• Watchers: 3
• Forks: 9
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        

# 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](https://aashishrai3799.github.io/3DFaceCAM)] [[Video](https://drive.google.com/file/d/1PqIN4Rzp4vapWs2pUegUEoMhg4lM2Smy/view?usp=sharing)] [[Colab Demo](#)] [[Arxiv](https://arxiv.org/abs/2208.14263)] 

![](3dfacecam.gif)

![](arch.png)

## Testing

Conda environment: Refer environment.yml

Download pre-trained weights and put the "checkpoints" folder in the main directory. [[Link](https://drive.google.com/file/d/1hK31wVAoieRiVFydPxnx0MVpx6AnWN1-/view?usp=sharing)]

- 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](https://facescape.nju.edu.cn/Page_Download/)]. The dataset is restricted to be used for non-commercial research only. Learn more about Facescape License [[Link](https://facescape.nju.edu.cn/static/License_Agreement.pdf)].

### 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](https://github.com/aashishrai3799/3DFaceCAM/blob/main/LICENSE)]. Quick summary available at [[Link](https://www.tldrlegal.com/l/x11)].

## 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}

}

```