  This paper presents Generative Adversarial Talking Head (GATH), a novel deep
generative neural network that enables fully automatic facial expression
synthesis of an arbitrary portrait with continuous action unit (AU)
coefficients. Specifically, our model directly manipulates image pixels to make
the unseen subject in the still photo express various emotions controlled by
values of facial AU coefficients, while maintaining her personal
characteristics, such as facial geometry, skin color and hair style, as well as
the original surrounding background. In contrast to prior work, GATH is purely
data-driven and it requires neither a statistical face model nor image
processing tricks to enact facial deformations. Additionally, our model is
trained from unpaired data, where the input image, with its auxiliary identity
label taken from abundance of still photos in the wild, and the target frame
are from different persons. In order to effectively learn such model, we
propose a novel weakly supervised adversarial learning framework that consists
of a generator, a discriminator, a classifier and an action unit estimator. Our
work gives rise to template-and-target-free expression editing, where still
faces can be effortlessly animated with arbitrary AU coefficients provided by
the user.
