  In this paper, we introduce a novel deep learning method for photo-realistic
manipulation of the emotional state of actors in "in-the-wild" videos. The
proposed method is based on a parametric 3D face representation of the actor in
the input scene that offers a reliable disentanglement of the facial identity
from the head pose and facial expressions. It then uses a novel deep domain
translation framework that alters the facial expressions in a consistent and
plausible manner, taking into account their dynamics. Finally, the altered
facial expressions are used to photo-realistically manipulate the facial region
in the input scene based on an especially-designed neural face renderer. To the
best of our knowledge, our method is the first to be capable of controlling the
actor's facial expressions by even using as a sole input the semantic labels of
the manipulated emotions, while at the same time preserving the speech-related
lip movements. We conduct extensive qualitative and quantitative evaluations
and comparisons, which demonstrate the effectiveness of our approach and the
especially promising results that we obtain. Our method opens a plethora of new
possibilities for useful applications of neural rendering technologies, ranging
from movie post-production and video games to photo-realistic affective
avatars.
