  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.
