Learning Physics-Based Motion Style with Nonlinear Inverse Optimization

This paper presents a novel physics-based representation of real- istic character motion. The dynamical model incorporates several factors of locomotion derived from the biomechanical literature, in- cluding relative preferences for using some muscles more than oth- ers, elastic mechanisms at joints due to the mechanical properties of tendons, ligaments, and muscles, and variable stiffness at joints de- pending on the task. When used in a spacetime optimization frame- work, the parameters of this model define a wide range of styles of natural human movement. Due to the complexity of biological motion, these style parame- ters are too difficult to design by hand. To address this, we introduce Nonlinear Inverse Optimization, a novel algorithm for estimating optimization parameters from motion capture data. Our method can extract the physical parameters from a single short motion se- quence. Once captured, this representation of style is extremely flexible: motions can be generated in the same style but perform- ing different tasks, and styles may be edited to change the physical properties of the body.