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Riggable 3D Face Reconstruction via In-Network Optimization

Source code for CVPR 2021 paper "Riggable 3D Face Reconstruction via In-Network Optimization".

[paper] [supp] [arXiv] [presen_video] [supp_video].

Installation

(1) Create an Anaconda environment.

conda env create -f env.yaml
conda activate INORig

(2) Clone the repository and install dependencies.

git clone https://github.com/zqbai-jeremy/INORig.git
cd INORig
pip install -r requirements_pip.txt

(3) Setup 3DMM

• Clone this repository, which is forked and modified from YadiraF/face3d, to "<INORig directory>/external/".

mkdir external
cd external
git clone https://github.com/zqbai-jeremy/face3d.git
cd face3d

• Setup face3d as in YadiraF/face3d.

• Download "Exp_Pca.bin" from Guo et al. (in "CoarseData" link of their repository) and copy to "<INORig directory>/external/face3d/examples/Data/BFM/Out/".

• Download "std_exp.txt" from Deng et al. and copy to "<INORig directory>/external/face3d/examples/Data/BFM/Out/".

(5) Download pre-trained model (Due to the sensitivity of face swapping, please email ziqian_bai@sfu.ca to request for the models. Please use your institution email and indicate in the email that you agree to only use the models for research purpose and not to share with 3rd parties. Sorry for the inconvenience and thank you for your understanding!) to "<INORig directory>/net_weights/". Need to create the folder. Unzip to get .pth files. "Ours.pth" is the basic version. "Ours(R).pth" is a more robust while less accurate version. Experiments in the paper are performed with these models.

Run Demo

• Modify directory paths in demo.py and run

cd <INORig_directory>
python demo.py

• Variables:

  • rig_img_dir: Folder contains images to build the rig.

  • src_vid_path: Video path to drive the rig.

  • out_dir: Folder for outputs. Reconstructions of images are in "<out_dir>/mesh" and "<out_dir>/visualization". Video reconstruction and retargeting are in "<out_dir>/videos".

• The example video clip "<INORig directory>/examples/videos/clip1.mp4" is from https://www.youtube.com/watch?v=ikAfrKf5A8I

Acknowledge

• Thank Ayush Tewari, Soubhik Sanyal and Jiaxiang Shang for helping the evaluations!

• Thank for the helpful tools from YadiraF/face3d!

Citation

@InProceedings{Bai_2021_CVPR,
    author    = {Bai, Ziqian and Cui, Zhaopeng and Liu, Xiaoming and Tan, Ping},
    title     = {Riggable 3D Face Reconstruction via In-Network Optimization},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {6216-6225}
}