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We present a top-down approach to simultaneously track and recognize articulated full-body human motion using learned action models that is robust to variations in style, lighting, background,occlusion and viewpoint. To this end, we introduce the hierarchical variable transition hidden Markov model (HVT-HMM) that is a three-layered extension of the variable transition hidden Markov model (VTHMM). The top-most layer of the HVT-HMM represents the composite actions and contains a single Markov chain, the middle layer represents the primitive actions which are modeled using a VTHMM whose state transition probability varies with time and the bottom-most layer represents the body pose transitions using a HMM. We represent the pose using a 23D body model and present efficient learning and decoding algorithms for HVT-HMM. Further, in classical Viterbi decoding the entire sequence must be seen before the state at any instant can be recognized and hence can potentially have large latency for long video sequences. In order to address this we use a variable window approach to decoding with very low latency. We demonstrate our methods first in a domain for recognizing two-handed gestures and then in a domain with actions involving articulated motion of the entire body. Our approach shows 90-100% action recognition in both domains and runs at real-time (ap 30 fps) with very low average latency (ap 2 frames).