Human Motion Reconstruction from Sparse 3D Motion Sensors Using Kernel CCA-Based Regression

This paper presents a real-time performance animation system that reproduces full-body character animation based on sparse three-dimensional (3D) motion sensors on a performer. Producing faithful character animation from this setting is a mathematically ill-posed problem, because input data from the sensors are not sufficient to determine the full degrees of freedom of a character. Given the input data from 3D motion sensors, we select similar poses from a motion database and build an online local model that transforms the low-dimensional input signal into a high-dimensional character pose. A regression method based on kernel canonical correlation analysis (CCA) is employed, because it effectively handles a wide variety of motions. Examples show that various human motions are naturally reproduced by the proposed method.