Semantic 3D motion retargeting for facial animation

We present a system for realistic facial animation that decomposes facial motion capture data into semantically meaningful motion channels based on the Facial Action Coding System. A captured performance is retargeted onto a morphable 3D face model based on a semantic correspondence between motion capture and 3D scan data. The resulting facial animation reveals a high level of realism by combining the high spatial resolution of a 3D scanner with the high temporal accuracy of motion capture data that accounts for subtle facial movements with sparse measurements. Such an animation system allows us to systematically investigate human perception of moving faces. It offers control over many aspects of the appearance of a dynamic face, while utilizing as much measured data as possible to avoid artistic biases. Using our animation system, we report results of an experiment that investigates the perceived naturalness of facial motion in a preference task. For expressions with small amounts of head motion, we find a benefit for our part-based generative animation system over an example-based approach that deforms the whole face at once.