Learning Switching Linear Models of Human Motion
Vladimir Pavlović, James Matthew Rehg, John MacCormick · 2000
The human figure exhibits complex and rich dynamic behavior that is both nonlinear and time-varying. Effective models of human dynamics can be learned from motion capture data using switching linear dynamic system (SLDS) models. We present results for human motion synthesis, classification, and visual tracking using learned SLDS models. Since exact inference in SLDS is intractable, we present three approximate inference algorithms and compare their performance. In particular, a new variational inference algorithm is obtained by casting the SLDS model as a Dynamic Bayesian Network. Classification experiments show the superiority of SLDS over conventional HMM's for our problem domain. 1 Introduction The human figure exhibits complex and rich dynamic behavior. Dynamics are essential to the classification of human motion (e.g. gesture recognition) as well as to the synthesis of realistic figure motion for computer graphics. In visual tracking applications, dynamics can provide a p...