SADRNet

Paper link: SADRNet: Self-Aligned Dual Face Regression Networks for Robust 3D Dense Face Alignment and Reconstruction

Requirements

python                 3.6.2
matplotlib             3.1.1  
Cython                 0.29.13
numba                  0.45.1
numpy                  1.16.0   
opencv-python          4.1.1
Pillow                 6.1.0                 
pyrender               0.1.33                
scikit-image           0.15.0                
scipy                  1.3.1
torch                  1.2.0                 
torchvision            0.4.0

Pretrained model

Link: https://drive.google.com/file/d/1mqdBdVzC9myTWImkevQIn-AuBrVEix18/view?usp=sharing .

Please put it under data/saved_model/SADRNv2/.

Please set ./SADRN as the working directory when running codes in this repo.

Predicting

• Put images under data/example/.

• Run src/run/predict.py.

The network takes cropped-out 256×256×3 images as the input.

Training

• Download 300W-LP and AFLW2000-3D at http://www.cbsr.ia.ac.cn/users/xiangyuzhu/projects/3ddfa/main.htm .

• Extract them into 'data/packs/AFLW2000' and 'data/packs/300W_LP'

• Please refer to face3d to prepare BFM data. And move the generated files in Out/ to data/Out/

• Run src/run/prepare_dataset.py, it will take several hours.

• Run train_block_data.py. Some training settings are included in config.py and src/configs.

Acknowledgements

We especially thank the contributors of the face3d codebase for providing helpful code.