  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.
