Motion Compression using Principal Geodesics Analysis

TLDR

The entertainment industry’s growing demand for large amounts of human animation data necessitates compressing motion capture sequences to ease storage and transmission. The authors propose a novel lossy compression method for human motion data that exploits both temporal and spatial coherence. The method first approximates the pose manifold with Principal Geodesics Analysis, then uses an iterative minimization algorithm to find poses satisfying end‑effector constraints for real‑time inverse kinematics, storing only the manifold parametrization along with compressed end‑effector and root joint trajectories, and reconstructs poses via the IK algorithm. Experiments demonstrate that the approach achieves substantial compression rates while incurring only minor reconstruction and perceptual errors.

Abstract

Abstract Due to the growing need for large quantities of human animation data in the entertainment industry, it has become a necessity to compress motion capture sequences in order to ease their storage and transmission. We present a novel, lossy compression method for human motion data that exploits both temporal and spatial coherence. Given one motion, we first approximate the poses manifold using Principal Geodesics Analysis (PGA) in the configuration space of the skeleton. We then search this approximate manifold for poses matching end‐effectors constraints using an iterative minimization algorithm that allows for real‐time, data‐driven inverse kinematics. The compression is achieved by only storing the approximate manifold parametrization along with the end‐effectors and root joint trajectories, also compressed, in the output data. We recover poses using the IK algorithm given the end‐effectors trajectories. Our experimental results show that considerable compression rates can be obtained using our method, with few reconstruction and perceptual errors.