GANimation: Anatomically-aware Facial Animation from a Single Image

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.

Prerequisites

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

Data Preparation

The code requires a directory containing the following files:

• imgs/: folder with all image
• aus_openpose.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 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:

python data/prepare_au_annotations.py

Run

To train:

bash launch/run_train.sh

To test:

python test --input_path path/to/img

Citation

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={ECCV},
year={2018}
}