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2000

Learning Switching Linear Models of Human Motion

10 years 4 months ago
Learning Switching Linear Models of Human Motion
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.
Vladimir Pavlovic, James M. Rehg, John MacCormick
Added 01 Nov 2010
Updated 01 Nov 2010
Type Conference
Year 2000
Where NIPS
Authors Vladimir Pavlovic, James M. Rehg, John MacCormick
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