Realistic 3D facial animation parameters from mirror-reflected multi-view video

Abstract

A robust, accurate and inexpensive approach to estimate 3D facial motion from multi-view video is proposed, where two mirrors located near one's cheeks can reflect the side views of markers on one face. Nice properties of mirrored images are utilized to simplify the proposed tracking algorithm significantly, while a Kalman filter is employed to reduce the noise and to predict the occluded marker positions. More than 50 markers on one face are continuously tracked at 30 frames per second. The estimated 3D facial motion data has been practically applied to our facial animation system. In addition, the dataset of facial motion can also be applied to the analysis of co-articulation effects, facial expressions, and audio-visual hybrid recognition system.