  Model based methods to marker-free motion capture have a very high
computational overhead that make them unattractive. In this paper we describe a
method that improves on existing global optimization techniques to tracking
articulated objects. Our method improves on the state-of-the-art Annealed
Particle Filter (APF) by reusing samples across annealing layers and by using
an adaptive parametric density for diffusion. We compare the proposed method
with APF on a scalable problem and study how the two methods scale with the
dimensionality, multi-modality and the range of search. Then we perform
sensitivity analysis on the parameters of our algorithm and show that it
tolerates a wide range of parameter settings. We also show results on tracking
human pose from the widely-used Human Eva I dataset. Our results show that the
proposed method reduces the tracking error despite using less than 50% of the
computational resources as APF. The tracked output also shows a significant
qualitative improvement over APF as demonstrated through image and video
results.
