A Region-based Facial Expression Cloning

In this paper, we propose a region-based blend shape approach to facial expression cloning that automatically extracts a set of coherently-moving regions of the source face model and their counterparts in the target face model without any extra input data except the source and target key face models and their correspondence. We represent a face mesh as a mass-spring network to define two measures, potential energy and movement coherency, that quantify the spring energy at a mass point and the movement similarity between a pair of mass points, respectively. Based on these measures, we develop three schemes that together facilitate our approach: the automatic extraction, correspondence establishment, and grouping of the feature points on the source and target face models. From the user’s point of view, our approach greatly reduces the animator’s workload while enjoying the advantages inherent in blend shape-based facial expression cloning.