针对3维人脸动画应用中,需要手工事先标定肌肉模型的控制点、工作区域和设置各种计算参数,造成工作量大、修改困难、移植性差等弊端,提出自动构造各种肌肉模型及确定它们计算参数的方法。研究工作包括:综合运用法向量变化率、高斯曲率、高斯纹理模型等参数研究3维人脸几何及纹理特征的快速检测方法;设计基于邻域生长和候选点聚类分析的识别算法来识别人脸五官部位的特征点;在此基础上,自动确定各种肌肉模型的位置结构、工作区域和计算参数,实现人脸动画所需的肌肉模型构造和装配的自动化。应用工作结果表明,基于特征识别的3维人脸动画肌肉模型自动构造方法移植性好、精度较高,提高了动画建模工作的效率。

Considering the work needed for constructing muscle models artificially,setting their control nodes, and adjusting their computer parameters, we present a method to construct the muscle model automatically and to generate the model calculation parameters for 3D facial animation. We developed a robust facial features recognition algorithm to extract the geometry and texture feature vertices. In the geometry feature recognition process, we adopt synthetically several constraints related to the Gaussian curvature and surface normal value to extract the candidate vertices. In the texture feature recognition process, we use the Gaussian Mixture Model of CrCgCb to extract the feature vertices. Then, clustering procedures are applied to gain the final feature vertices. Finally,using the 13 geometry feature vertices and 8 texture feature vertices extracted by the recognition algorithm,we automatically construct the muscle models for the real-time facial animation.The experimental results demonstrate a matching rate over 90% compared with the landmark vertices made by an artist. The application work indicates that the process of automated muscle model construction based on the feature recognition algorithm fit in with different human head geometries very well. On this basis, we synthesize a group of characteristic facial expressions and mouth shapes with higher realism in real time.