Forecasting Human Pose and Motion with Multibody Dynamic Model

Song Cao, Ram Nevatia

Abstract: Understanding human motion with dynamics is in its infancy, but it is a highly promising approach in computer vision, robotics and computer graphics. We propose a Multibody Dynamic Model (MDM) which estimates poses and motions through analyzing forces-the intrinsic motivation for motion. With the 23 degrees of freedom Multibody Dynamic Model, we analyze human motion dynamics in the whole body, and then forecast human motion or pose in occluded or non-captured circumstances. Our two main contributions are essential for understanding human motion with dynamics. The first one is to provide effective representations and computational models for dynamic analysis of human motion in the whole body, via the intrinsic connection between force and motion in the biomechanical system. The second contribution is to offer a more natural method to forecast pose and motion with the estimated forces. In our experiments, MDM has been successfully applied to running, jumping and other challenging sports activities.