Cloning facial expressions with user-defined example models

Abstract

A main trend of recent computer animation is reusing existing motion data to produce a new animation. However, most of proposed retargeting approaches mainly focus on articulated body motions. In this thesis, we present a novel example-based approach for the cloning facial expression of a source model to a new model while reflecting the characteristic features of the new model in a resulting animation. Our method adopts an example-based approach. Given the source example models and their corresponding target example models created by an animator, we parameterize the target example models using the source example models and predefine the weight functions for the parameterized target example models based on radial basis functions. At runtime, given a source model, we compute the parameter vector of the new model to evaluate the weight functions for the target example models at their parameter vectors and obtain the new model by blending them with respect to their weight values. The resulting animation preserves the facial expressions of the source model as well as the characteristic features of the new model. Our approach provides the real-time performance to be applied to the applications such as video games and internet broadcasting.