Detailed Riggable 3D Face Prediction

[CVPR2020 Paper]    [FaceScape Dataset]

Requirements

• The project is tested on ubuntu 18.04 with python 3.6, PyTorch 1.6.0, torchvision 0.7.0.
• Bilinear model can be downloaded from FaceScape dataset by applying the license key (for non-commercial research only). Learn about FaceScape dataset on github page.

Usage

1. We recomend using Anaconda to create a new enviroment:

conda create -n 3dface python=3.6
conda activate 3dface

Install dependecies:

pip install -r requirements.txt

Refer to the guide to install the PyTorch 1.6.0.

2. Download the bilinear model from FaceScape dataset. After getting access to the dataset, download 'facescape_bilinear_model_v1_3.zip' from the download page. Then put 'core_847_50_52.npy' and 'factors_id_847_50_52.npy' into './predef' folder.

3. Download the pretrained model from Google Drive, unzip the file and place 'dpmap_rig_net_G.pth' and 'dpmap_single_net_G.pth' in './checkpoints' folder.

4. Run the demo code.

python main.py --name dpmap_single --input ./demo/input --output ./demo/output --gpu_ids 0 --render

The results will be saved in the folder './demo/output/'. Run "python main.py -h" for more usages. We provide a simple renderer to visualize results in the demo. Displacement maps can also be loaded and visualized in MAYA, ZBrush, 3DS MAX, etc. The results in our paper are rendered in ZBrush and the scale is set to 0.014.

You can set '--name dpmap_rig' to predict displacement maps corresponding to the 20 key expressions. The input image should be in nearly neutral expression for this option.

Note that the base model fitting results may be different from that in our paper, because our landmark detector cannot be released due to the licenses. So we used the publicly available dlib detector as substituent and simplified the fitting algorithm.

Citation

Please cite the following paper if this project helps your research:

@InProceedings{yang2020facescape,
  author = {Yang, Haotian and Zhu, Hao and Wang, Yanru and Huang, Mingkai and Shen, Qiu and Yang, Ruigang and Cao, Xun},
  title = {FaceScape: A Large-Scale High Quality 3D Face Dataset and Detailed Riggable 3D Face Prediction},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {June},
  year = {2020},
  page = {601--610}}