Abstract

Synthesizing expressive facial animation is a very challenging topic within the graphics community. In this paper, we present an expressive facial animation synthesis system enabled by automated learning from facial motion capture data. Accurate 3D motions of the markers on the face of a human subject are captured while he/she recites a predesigned corpus, with specific spoken and visual expressions. We present a novel motion capture mining technique that "learns” speech coarticulation models for diphones and triphones from the recorded data. A Phoneme-Independent Expression Eigenspace (PIEES) that encloses the dynamic expression signals is constructed by motion signal processing (phoneme-based time-warping and subtraction) and Principal Component Analysis (PCA) reduction. New expressive facial animations are synthesized as follows: First, the learned coarticulation models are concatenated …

Date

2006

Authors

Zhigang Deng, Ulrich Neumann, John P Lewis, Tae-Yong Kim, Murtaza Bulut, Shrikanth Narayanan

Journal

IEEE transactions on visualization and computer graphics

Volume

12

Issue

6

Pages

1523-1534

Publisher

IEEE