  Face transfer animates the facial performances of the character in the target
video by a source actor. Traditional methods are typically based on face
modeling. We propose an end-to-end face transfer method based on Generative
Adversarial Network. Specifically, we leverage CycleGAN to generate the face
image of the target character with the corresponding head pose and facial
expression of the source. In order to improve the quality of generated videos,
we adopt PatchGAN and explore the effect of different receptive field sizes on
generated images.
