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Code for DSFNet: Dual Space Fusion Network for Occlusion-Robust Dense 3D Face Alignment

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DSFNet

Paper link: https://arxiv.org/abs/2305.11522
Project link: https://lhyfst.github.io/dsfnet/

Requirements

python                    3.6.13
pytorch                   1.7.1
cudatoolkit               10.1.243
imageio                   2.15.0
numpy                     1.19.2
opencv-python             4.7.0.72
PyYAML                    6.0
scikit-image              0.17.2
torchvision               0.8.2
tqdm                      4.64.1
trimesh                   3.22.1

You can easily prepare the conda environment by conda create --name DSFNet --file requirements.txt

Prepare

Evaluation

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

  • Follow SADRNet to crop images and prepare the image directory. Or you can download the cropped images at link. Put them at data/dataset/AFLW2000_crop.

  • Run src/run/predict.py. In the returned text, nme3d, rec, MAE are the results of dense 3D dense face alignment, reconstruction, and head pose estimation.

Acknowledgements

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

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Code for DSFNet: Dual Space Fusion Network for Occlusion-Robust Dense 3D Face Alignment

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