Compression of Human Motion Animation Using the Reduction of Interjoint Correlation

Abstract

We propose two compression methods for the human motion in 3D space, based on the forward and inverse kinematics. In a motion chain, a movement of each joint is represented by a series of vector signals in 3D space. In general, specific types of joints such as end effectors often require higher precision than other general types of joints in, for example, CG animation and robot manipulation. The first method, which combines wavelet transform and forward kinematics, enables users to reconstruct the end effectors more precisely. Moreover, progressive decoding can be realized. The distortion of parent joint coming from quantization affects its child joint in turn and is accumulated to the end effector. To address this problem and to control the movement of the whole body, we propose a prediction method further based on the inverse kinematics. This method achieves efficient compression with a higher compression ratio and higher quality of the motion data. By comparing with some conventional methods, we demonstrate the advantage of ours with typical motions.