A semi-supervised framework for topology preserving performance-driven facial animation

In this paper, we divide performance-driven facial animation into two data transformation problems, facial expression retargeting and face driving, and report a semi-supervised framework to solve the two problems. The objective function includes two parts. In the first part, we unify the temporal and geometrical characteristics of facial expressions and face models as topology characteristics, and preserve the topology characteristics in manifold subspace during data transformation. In the second part, some given data are used as labels to guide the transformation. The proposed semi-supervised framework can be efficiently solved by a least square method. Experimental results show that the proposed framework outperforms existing methods in both facial expression retargeting and face driving.