Semi-Supervised Facial Animation Retargeting

This paper presents a system for facial animation retargeting that allows learning a high-quality mapping between motion capture data and arbitrary target characters. We address one of the main challenges of existing example-based retargeting methods, the need for a large number of accurate training examples to define the correspondence between source and target expression spaces. We show that this number can be significantly reduced by leveraging the information contained in unlabeled data, i.e. facial expressions in the source or target space without corresponding poses. In contrast to labeled samples that require time-consuming and error-prone manual character posing, unlabeled samples are easily obtained as frames of motion capture recordings or existing animations of the target character. Our system exploits this information by learning a shared latent space between motion capture and character parameters in a semi-supervised manner. We show that this approach is resilient to noisy input and missing data and significantly improves retargeting accuracy. To demonstrate its applicability, we integrate our algorithm in a performance-driven facial animation system.