Kinematics Based Motion Compression for Human Figure Animation

Abstract

The paper presents a technique for compression of motion data using an inverse kinematics algorithm. In a motion chain, the 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. When compressing these motion data, the distortion of a parent joint due to quantization in turn affects its child joint and is accumulated to the end effector. To address this problem and control the movement of the whole body, we propose a prediction method based on inverse kinematics. Our method achieves efficient compression with a high compression rate and high quality of the motion data. By comparing with a conventional prediction, we demonstrate the advantage of ours with some examples.