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.
Highlights
Facial animation is one alternative for enabling natural human-computer interaction
We introduce a novel MPEG-4 based 3D facial animation framework and the animation system driving by facial animation parameters (FAPs) that produced from camera videos
A simple ERI’s parameterized method is adopted for generating an FAP driving model by the support vector regression and it is a statistic model based on MPEG-4
Summary
Facial animation is one alternative for enabling natural human-computer interaction. Computer facial animation has applications in many fields. Among the issues concerning the realism of synthesized facial animation, humanlike expression is critical. Facial expression analysis and synthesis are an active and challenging research topic in computer vision, impacting important applications in areas such as human-computer interaction and data-driven animation. We introduce a novel MPEG-4 based 3D facial animation framework and the animation system driving by FAP that produced from camera videos. A simple ERI’s parameterized method is adopted for generating an FAP driving model by the support vector regression and it is a statistic model based on MPEG-4. The results of FAPs can be used to drive the 3D face model defined by MPEG-4 standard.
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