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Efficient monocular human pose tracking in dynamic scenes is an important problem. Existing pose tracking methods either use activity priors to restrict the search space, or use generative body models with weak kinematic constraints to infer pose over multiple frames; these often tends to be slow. We develop an efficient algorithm to track human pose by estimating multi-frame body dynamics without activity priors. We present a monte-carlo approximation of the body dynamics using spatio-temporal distributions over part tracks. To obtain tracks that favor kinematically feasible body poses, we propose a novel Â¿kinematically constrainedÂ¿ particle filtering approach which results in more accurate pose tracking than other stochastic approaches that use single frame priors. We demonstrate the effectiveness of our approach on videos with actors performing various actions in indoor dynamic scenes.