Incorporating Physics Principles for Precise Human Motion Prediction

A variety of real-world applications rely on accurate predictions of 3D human motion from their past observations. While existing methods have made notable progress, their predictions over subsecond horizons can still be off by many centimeters. In this paper, we argue that achieving precise human motion prediction requires characterizing the fundamental physics principles governing body movements. We introduce PhysMoP, a novel framework that incorporates Physics for human Motion Prediction. PhysMoP estimates the body configuration of the next frame by solving the Euler-Lagrange equations, a set of Ordinary Different Equations describing the physical motion rules. To limit the inherent problem of error accumulation over time, PhysMoP leverages a data-driven model and iteratively guides the physics-based prediction via a fusion model. Through extensive experiments, we demonstrate that PhysMoP significantly outperforms existing approaches at subsecond prediction horizons. For example, at a prediction horizon of 80 msec, PhysMoP outperforms traditional data-driven approaches by a factor of 10 or more.