Learning Human Motion Prediction via Stochastic Differential Equations
This work addresses the problem of improving predictive accuracy for human motion in machine intelligence and human-machine interaction systems, representing a novel method for a known bottleneck.
The paper tackles human motion prediction by modeling skeletal joint motion with stochastic differential equations and path integrals, achieving a 12.48% accuracy improvement over state-of-the-art methods on benchmark datasets.
Human motion understanding and prediction is an integral aspect in our pursuit of machine intelligence and human-machine interaction systems. Current methods typically pursue a kinematics modeling approach, relying heavily upon prior anatomical knowledge and constraints. However, such an approach is hard to generalize to different skeletal model representations, and also tends to be inadequate in accounting for the dynamic range and complexity of motion, thus hindering predictive accuracy. In this work, we propose a novel approach in modeling the motion prediction problem based on stochastic differential equations and path integrals. The motion profile of each skeletal joint is formulated as a basic stochastic variable and modeled with the Langevin equation. We develop a strategy of employing GANs to simulate path integrals that amounts to optimizing over possible future paths. We conduct experiments in two large benchmark datasets, Human 3.6M and CMU MoCap. It is highlighted that our approach achieves a 12.48% accuracy improvement over current state-of-the-art methods in average.