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

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

• Download BFM_UVspace_patch.npy. Put it under data/uv_data/

• Download pretrained model. Put it under data/saved_model/.

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.