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Subtle Facial Expression Synthesis using Motion Manifold Embedding and Nonlinear Decomposable Generative Models

Facial motions convey personal characteristics and subtle emotional states. This paper presents a new framework to model facial motions of different people with multiple expression types from high resolution facial expression tracking data. We also provide a mechanism to animate subtle facial expressions based on video sequences. A conceptual motion manifold is used for a unified representation of facial motion dynamics. Subtle local motions in facial expressions are modeled by nonlinear mapping using empirical kernel map from an embedding manifold. We represent facial expressions in different people, as well as different expression type by a nonlinear decomposable generative model using multilinear analysis of the nonlinear mappings coefficient space. We can synthesize high resolution facial motions based on tracking of facial motions in video sequences by estimation of the model parameters.