This repository is only a re-implementation of 《Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network》. If you use this code, please observe the license of PRNet and 3DDFA. Please make sure that this code is only used for research and non-commercial purposes. I hope this re-implementation can help you.
python 3.6.9
opencv-python 4.1
pillow 6.1
pyrender 0.1.32
pytorch 1.1.0
scikit-image 0.15.0
scikit-learn 0.21.3
scipy 1.3.1
tensorboard 2.0.0
torchvision 0.3.0
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Please refer to face3d to prepare BFM data. And move the generated files in Out/ to data/Out/
Editor note: Simply rundvc pullif you are strong enough. -
Download databases from 3DDFA. Put the raw dataset in data/images (e.g. data/images/AFLW2000):
- AFLW2000:
wget http://www.cbsr.ia.ac.cn/users/xiangyuzhu/projects/3DDFA/Database/AFLW2000-3D.zip. - 300WLP: create a shortcut from 300WLP to your Google Drive, then use rclone.
- (maybe) You have to compile Cython module before running
processor.py:
cd utils/faceutil/mesh/cython.python3 setup.py build_ext --inplace.
- Run processor.py to generate UV position maps. I recommend you to use the following instructions:
python processor.py -i=data/images/AFLW2000 -o=data/images/AFLW2000-crop -f=True -v=True --isOldKpt=True
python processor.py -i=data/images/300W_LP -o=data/images/300W_LP-crop --thread=16
It takes about 2-4 hours depending on your working machine.
- Train PRNet (adjust argument if needed):
sh scripts/train.sh
- Inference: In progress. Stay tuned.
Result examples:
