Learning 3D Face Deformation Model: Methods and Applications
Zhen Wen (University of Illinois at Urbana Chapmaign, USA), Pengyu Hong (Harvard University, USA), Jilin Tu (University of Illinois at Urbana Champaign, USA) and Thomas S. Huang (University of Illinois at Urbana Champaign, USA)
Copyright: © 2004
This chapter presents a unified framework for machine-learning-based facial deformation modeling, analysis and synthesis. It enables flexible, robust face motion analysis and natural synthesis, based on a compact face motion model learned from motion capture data. This model, called Motion Units (Muss), captures the characteristics of real facial motion. The MU space can be used to constrain noisy low-level motion estimation for robust facial motion analysis. For synthesis, a face model can be deformed by adjusting the weights of Mus. The weights can also be used as visual features to learn audio-to-visual mapping using neural networks for real-time, speech-driven, 3D face animation. Moreover, the framework includes parts-based MUs because of the local facial motion and an interpolation scheme to adapt MUs to arbitrary face geometry and mesh topology. Experiments show we can achieve natural face animation and robust non-rigid face tracking in our framework.