A new method of 3D facial expression animation

Abstract

Animating expressive facial animation is a very challenging topic within the graphics community. In this paper, we introduce a novel ERI (expression ratio image) driving framework based on SVR and MPEG-4 for automatic 3D facial expression animation. Through using the method of support vector regression (SVR), the framework can learn and forecast the regression relationship between the facial animation parameters (FAPs) and the parameters of expression ratio image. Firstly, we build a 3D face animation system driven by FAP. Secondly, through using the method of principle component analysis (PCA), we generate the parameter sets of eigen-ERI space, which will rebuild reasonable expression ratio image. Then we learn a model with the support vector regression mapping, and facial animation parameters can be synthesized quickly with the parameters of eigen-ERI. Finally, we implement our 3D face animation system driving by the result of FAP and it works effectively. © 2014 Shuo Sun and Chunbao Ge.