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In this paper, we introduce a novel deep learning method for photo-realisticmanipulation of the emotional state of actors in "in-the-wild" videos. Theproposed method is based on a parametric 3D face representation of the actor inthe input scene that offers a reliable disentanglement of the facial identityfrom the head pose and facial expressions. It then uses a novel deep domaintranslation framework that alters the facial expressions in a consistent andplausible manner, taking into account their dynamics. Finally, the alteredfacial expressions are used to photo-realistically manipulate the facial regionin the input scene based on an especially-designed neural face renderer. To thebest of our knowledge, our method is the first to be capable of controlling theactor's facial expressions by even using as a sole input the semantic labels ofthe manipulated emotions, while at the same time preserving the speech-relatedlip movements. We conduct extensive qualitative and quantitative evaluationsand comparisons, which demonstrate the effectiveness of our approach and theespecially promising results that we obtain. Our method opens a plethora of newpossibilities for useful applications of neural rendering technologies, rangingfrom movie post-production and video games to photo-realistic affectiveavatars.

Neural Emotion Director: Speech-preserving semantic control of facial expressions in "in-the-wild" videos