  The face reenactment is a popular facial animation method where the person's
identity is taken from the source image and the facial motion from the driving
image. Recent works have demonstrated high quality results by combining the
facial landmark based motion representations with the generative adversarial
networks. These models perform best if the source and driving images depict the
same person or if the facial structures are otherwise very similar. However, if
the identity differs, the driving facial structures leak to the output
distorting the reenactment result. We propose a novel Facial Attribute
Controllable rEenactment GAN (FACEGAN), which transfers the facial motion from
the driving face via the Action Unit (AU) representation. Unlike facial
landmarks, the AUs are independent of the facial structure preventing the
identity leak. Moreover, AUs provide a human interpretable way to control the
reenactment. FACEGAN processes background and face regions separately for
optimized output quality. The extensive quantitative and qualitative
comparisons show a clear improvement over the state-of-the-art in a single
source reenactment task. The results are best illustrated in the reenactment
video provided in the supplementary material. The source code will be made
available upon publication of the paper.
