CycleGAN Face-off
This work addresses video-based facial expression transfer for entertainment or communication applications, but it is incremental as it builds on existing CycleGAN methods.
The paper tackled the problem of unsupervised face-off style transfer by improving CycleGAN to capture facial expressions and head poses, resulting in higher consistency and stability in generated videos.
Face-off is an interesting case of style transfer where the facial expressions and attributes of one person could be fully transformed to another face. We are interested in the unsupervised training process which only requires two sequences of unaligned video frames from each person and learns what shared attributes to extract automatically. In this project, we explored various improvements for adversarial training (i.e. CycleGAN[Zhu et al., 2017]) to capture details in facial expressions and head poses and thus generate transformation videos of higher consistency and stability.