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We propose an approach to incorporating dynamic models into the human body tracking process that yields full 3--D reconstructions from monocular sequences. We formulate the tracking problem is terms of minimizing a differentiable criterion whose differential structure is rich enough for successful optimization using a single-hypothesis hill-climbing approach as opposed to a multi-hypotheses probabilistic one. In other words, we avoid the computational complexity of multi-hypotheses algorithms while obtaining excellent results under challenging conditions. To demonstrate this, we focus on monocular tracking of a golf swing from ordinary videos. It involves both dealing with potentially very different swing styles, recovering arm motions that are perpendicular to the camera plane and handling strong self-occlusions.