Data-driven facial animation via semi-supervised local patch alignment.

This paper reports a novel data-driven facial animation technique which drives a neutral source face to get the expressive target face using a semi-supervised local patch alignment framework. We define the local patch and assume that there exists a linear transformation between a patch of the target face and the intrinsic embedding of the corresponding patch of the source face. Based on this assumption, we compute the intrinsic embeddings of source patches and align these embeddings to form the result. During the course of alignment, we use a set of motion data as shape regularizer to impel the result to approach the unknown target face. The intrinsic embedding can be computed through both locally linear embedding and local tangent space alignment. Experimental results indicate that the proposed framework can obtain decent face driving results. Quantitative and qualitative evaluations of the proposed framework demonstrate its superiority to existing methods. HighlightsAchieve facial animation through manifold-based method.The local patch is defined by the geometry of the face to capture the local topology.This is a semi-supervised framework which can be solved by least square method.