Combining active learning and local patch alignment for data-driven facial animation with fine-grained local detail

Abstract

Data-driven facial animation has attracted considerable attention from both academic and industrial communities in recent years. Typically, the motion data used to animate the faces are derived from either 3D or 2D facial features whose positions on the face are determined according to experience. There still lacks an automatic approach to determine the optimal positions of the features to face deformation, and current face deformation methods are incapable of providing fine-grained local geometric characteristics. This paper proposes a novel scheme for face animation in which an active learning method based on Locally Linear Reconstruction algorithm is exploited to determine the optimal feature positions on the face for face deformation, and the Semi-Supervised Local Patch Alignment algorithm is subsequently used to deform the face with the selected features according to the optimal feature positions. The active learning model can be solved by a sequential approach, and the Semi-Supervised Local Patch Alignment model can be addressed by a least-square method. Experimental results on various types of faces demonstrate the superiority of the proposed scheme to existing approaches in both feature points selection and fine-grained local characteristics preservation.