  The ability to accurately capture and express emotions is a critical aspect
of creating believable characters in video games and other forms of
entertainment. Traditionally, this animation has been achieved with artistic
effort or performance capture, both requiring costs in time and labor. More
recently, audio-driven models have seen success, however, these often lack
expressiveness in areas not correlated to the audio signal. In this paper, we
present a novel approach to facial animation by taking existing animations and
allowing for the modification of style characteristics. Specifically, we
explore the use of a StarGAN to enable the conversion of 3D facial animations
into different emotions and person-specific styles. We are able to maintain the
lip-sync of the animations with this method thanks to the use of a novel
viseme-preserving loss.
