Generative tracking of 3D human motion by hierarchical annealed genetic algorithm

We present a generative method for reconstructing 3D human motion from single images and monocular image sequences. Inadequate observation information in monocular images and the complicated nature of human motion make the 3D human pose reconstruction challenging. In order to mine more prior knowledge about human motion, we extract the motion subspace by performing conventional principle component analysis (PCA) on small sample set of motion capture data. In doing so, we also reduce the problem dimensionality so that the generative pose recovering can be performed more effectively. And, the extracted subspace is naturally hierarchical. This allows us to explore the solution space efficiently. We design an annealed genetic algorithm (AGA) and hierarchical annealed genetic algorithm (HAGA) for human motion analysis that searches the optimal solutions by utilizing the hierarchical characteristics of state space. In tracking scenario, we embed the evolutionary mechanism of AGA into the framework of evolution strategy for adapting the local characteristics of fitness function. We adopt the robust shape contexts descriptor to construct the matching function. Our methods are demonstrated in different motion types and different image sequences. Results of human motion estimation show that our novel generative method can achieve viewpoint invariant 3D pose reconstruction.