SAFA: Structure Aware Face Animation (3DV2021)

Official Pytorch Implementation of 3DV2021 paper: SAFA: Structure Aware Face Animation.

Getting Started

git clone https://github.com/Qiulin-W/SAFA.git

Installation

Python 3.6 or higher is recommended.

1. Install PyTorch3D

Follow the guidance from: https://github.com/facebookresearch/pytorch3d/blob/master/INSTALL.md.

2. Install other dependencies

To install other dependencies run:

pip install -r requirements.txt

Usage

1. Preparation

a. Download FLAME model, choose FLAME 2020 and unzip it, put generic_model.pkl under ./modules/data.

b. Download head_template.obj, landmark_embedding.npy, uv_face_eye_mask.png and uv_face_mask.png from DECA/data, and put them under ./module/data.

c. Download SAFA model checkpoint from Google Drive and put it under ./ckpt.

d. (Optional, required by the face swap demo) Download the pretrained face parser from face-parsing.PyTorch and put it under ./face_parsing/cp.

2. Demos

We provide demos for animation and face swap.

a. Animation demo

python animation_demo.py --config config/end2end.yaml --checkpoint path/to/checkpoint --source_image_pth path/to/source_image --driving_video_pth path/to/driving_video --relative --adapt_scale --find_best_frame

b. Face swap demo We adopt face-parsing.PyTorch for indicating the face regions in both the source and driving images.

For preprocessed source images and driving videos, run:

python face_swap_demo.py --config config/end2end.yaml --checkpoint path/to/checkpoint --source_image_pth path/to/source_image --driving_video_pth path/to/driving_video

For arbitrary images and videos, we use a face detector to detect and swap the corresponding face parts. Cropped images will be resized to 256*256 in order to fit to our model.

python face_swap_demo.py --config config/end2end.yaml --checkpoint path/to/checkpoint --source_image_pth path/to/source_image --driving_video_pth path/to/driving_video --use_detection

Training

We modify the distributed traininig framework used in that of the First Order Motion Model. Instead of using torch.nn.DataParallel (DP), we adopt torch.distributed.DistributedDataParallel (DDP) for faster training and more balanced GPU memory load. The training procedure is divided into two steps: (1) Pretrain the 3DMM estimator, (2) End-to-end Training.

3DMM estimator pre-training

CUDA_VISIBLE_DEVICES="0,1,2,3" python -m torch.distributed.launch --nproc_per_node 4 run_ddp.py --config config/pretrain.yaml

End-to-end Training

CUDA_VISIBLE_DEVICES="0,1,2,3" python -m torch.distributed.launch --nproc_per_node 4 run_ddp.py --config config/end2end.yaml --tdmm_checkpoint path/to/tdmm_checkpoint_pth

Evaluation/Inference

Video reconstrucion

python run_ddp.py --config config/end2end.yaml --checkpoint path/to/checkpoint --mode reconstruction

Image animation

python run_ddp.py --config config/end2end.yaml --checkpoint path/to/checkpoint --mode animation

3D face reconstruction

python tdmm_inference.py --data_dir directory/to/images --tdmm_checkpoint path/to/tdmm_checkpoint_pth

Dataset and Preprocessing

We use VoxCeleb1 to train and evaluate our model. Original Youtube videos are downloaded, cropped and splited following the instructions from video-preprocessing.

a. To obtain the facial landmark meta data from the preprocessed videos, run:

python video_ldmk_meta.py --video_dir directory/to/preprocessed_videos out_dir directory/to/output_meta_files

b. Extract images from videos

Citation

If you find our work useful to your research, please consider citing:

@article{wang2021safa,
  title={SAFA: Structure Aware Face Animation},
  author={Wang, Qiulin and Zhang, Lu and Li, Bo},
  journal={arXiv preprint arXiv:2111.04928},
  year={2021}
}

License

Please refer to the LICENSE file.

Acknowledgement

Codes are heavily borrowed from First Order Motion Model. Some codes are also borrowed from DECA, generative-inpainting-pytorch, face-parsing.PyTorch, video-preprocessing.

EricWang12 / SAFA