Adaptive gaussian mixture trajectory model for physical model control using motion capture data

Abstract

To enable the physically correct simulation of the interaction of a 3D character with its environment the internal joint forces of a physical model of the character need to be estimated. Recently, derivative-free sampling-based optimization methods, which treat the objective function as a black box, have shown great results for finding control signals for articulated figures in physics simulations. We present a novel sampling-based approach for the reconstruction of control signals for a rigid body model based on motion capture data that combines ideas of previous approaches. The algorithm optimizes control trajectories along a sliding window using the Covariance Matrix Adaption Evolution Strategy. The sampling distribution is represented as a mixture model with a dynamically selected number of clusters based on the variation detected in the samples. During the optimization we keep track of multiple states which enables the exploration of multiple paths. We evaluate the algorithm for the task of motion capture following using figures that were automatically generated from 3D character models.