Human motion can be understood on several levels. The most basic level is the notion that humans are collections of things that have predictable visual appearance. Next is the notion that humans exist in a physical universe, as a consequence of this, a large part of human motion can be modeled and predicted with the laws of physics. Finally there is the notion that humans utilize muscles to actively shape purposeful motion. We employ a recursive framework for real-time, 3-D tracking of human motion that enables pixel-level, probabilistic processes to take advantage of the contextual knowledge encoded in the higher-level models, including models of dynamic constraints on human motion. We will show that models of purposeful action arise naturally from this framework, and further, that those models can be used to improve the perception of human motion. Results are shown that demonstrate both qualitative and quantitative gains in tracking performance.