High-resolution animation of facial dynamics

This paper presents a framework for performance-based animation and retargeting of high-resolution face models from motion capture. A novel method is introduced for learning a mapping between sparse 3D motion capture markers and dense high-resolution 3D scans of face shape and appearance. A high-resolution facial expression space is learnt from a set of 3D face scans as a person specific morphable model. Sparse 3D face points sampled at the motion capture marker positions are used to build a corresponding low-resolution expression space to represent the facial dynamics from motion capture. Radial basis function interpolation is used to automatically map the low-resolution motion capture of facial dynamics to the high-resolution facial expression space. This produces a high-resolution facial animation with the detailed shape and appearance of real facial dynamics. Retargeting is introduced to transfer facial expressions to a novel subject captured from a single photograph or 3D scan. The subject specific high- resolution expression space is mapped to the novel subject based on anatomical differences in face shape. Results facial animation and retargeting demonstrate realistic animation of expressions from motion capture.