Realtime Performance Animation Using Sparse 3D Motion Sensors

This paper presents a realtime performance animation system that reproduces full-body character animation based on sparse 3D motion sensors on the performer. Producing faithful character animation from this setting is a mathematically ill-posed problem because input data from the sensors is not sufficient to determine the full degrees of freedom of a character. Given the input data from 3D motion sensors, we pick similar poses from the motion database and build an online local model that transforms the low-dimensional input signal into a high-dimensional character pose. Kernel CCA (Canonical Correlation Analysis)-based regression is employed as the model, which effectively covers a wide range of motion. Examples show that various human motions are naturally reproduced by our method.