Official implementation of GANimation. In this work we introduce a novel GAN conditioning scheme based on Action Units (AU) annotations, which describe in a continuous manifold the anatomical facial movements defining a human expression. Our approach permits controlling the magnitude of activation of each AU and combine several of them. For more information please refer to the paper.

This code was made public to share our research for the benefit of the scientific community. Do NOT use it for immoral purposes.

• Install PyTorch (version 0.3.1), Torch Vision and dependencies from http://pytorch.org
• Install requirements.txt (pip install -r requirements.txt)

The code requires a directory containing the following files:

• imgs/: folder with all image
• aus_openface.pkl: dictionary containing the images action units.
• train_ids.csv: file containing the images names to be used to train.
• test_ids.csv: file containing the images names to be used to test.

An example of this directory is shown in sample_dataset/.

To crop imgs and generate train_ids.csv and test_ids.csv in output_dir/csv run:

python pre_train --images_folder path_to_images --output_dir path_for_generated_files

To generate the aus_openface.pkl extract each image Action Units with OpenFace and store each output in a csv file the same name as the image. Then run:

cd OpenFace 
cd build
./bin/FaceLandmarkImg -fdir path_to_images -out_dir path_for_generated_files -aus

python data/prepare_au_annotations.py -ia input_aus_filesdir -op output_path

To train:

python train.py --data_dir path/to/dataset/ --name experiment_1 --batch_size 25 

To test:

python test --input_path path/to/img --output_dir path/to/output --face_aus_path path/of/ausfile

If you use this code or ideas from the paper for your research, please cite our paper:

@inproceedings{pumarola2018ganimation,
    title={GANimation: Anatomically-aware Facial Animation from a Single Image},
    author={A. Pumarola and A. Agudo and A.M. Martinez and A. Sanfeliu and F. Moreno-Noguer},
    booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
    year={2018}
}