Solving the tracking of an articulated structure in a reasonable time is a complex task mainly due to the high dimensionality of the problem. A new optimization method, called Stochastic Meta-Descent (SMD), based on gradient descent with adaptive and parameter specific step sizes was introduced recently to solve this challenging problem. While the local optimization works very well, reaching the global optimum is not guaranteed. We therefore propose a novel algorithm which combines the SMD optimization with a particle filter to form `smart particles`. After propagating the particles, SMD is performed and the resulting new particle set is included such that the original Bayesian distribution is not altered. The resulting `smart particle filter` (SPF) tracks high dimensional articulated structures with far fewer samples than previous methods. Additionally, it can handle multiple hypotheses, clutter and occlusion which pure optimization approaches have problems. The performance of the SMD particle filter is illustrated in challenging 3D hand tracking sequences demonstrating a better robustness and accuracy than those of a single SMD optimization or an annealed particle filter.