Multi-task audio-driven facial animation

We propose an effective method to solve multiple characters audio-driven facial animation (ADFA) problem in an end-to-end fashion via deep neural network. In this paper each character's ADFA considered as a single task, and our goal is to solve ADFA problem in multi-task setting. To this end, we present MulTaNet for multi-task audio-driven facial animation (MTADFA), which learns a cross-task unified feature mapping from audio-to-vertex that capture shared information across multiple related tasks, while try to find within-task prediction network encoding character-dependent topological information. Extensive experiments indicate that MulTaNet generates more natural-looking and stable facial animation, meanwhile shows better generalization capacity to unseen languages compare to previous approaches.