Marker-less human pose estimation and surface reconstruction using a segmented model

We propose a human motion tracking method for fast motion clips using synchronized multiple cameras. Our method is capable of extracting 3D articulated postures with 42 degrees of freedom through a sequence of visual hulls. We seek for the globally optimal solutions of the likelihood with the lo cal memorization about the "fitness" of each body segment. Our method avoids the local minimum problem efficiently by mean combination and articulated combination of parti cles selected based on the weights of the different body seg ments. We deform the template surface model using the mo tion tracking data by linear blend skinning (LBS). Then we recover the details of the surface by fitting the deformed sur face to 2D silhouettes.