Synthesis of Silhouettes and Visual Hull Reconstruction for Articulated Humans

In this paper, we propose a complete framework for improved synthesis and understanding of the human pose from a limited number of silhouette images. It combines the active image-based visual hull (IBVH) algorithm and a contour-based body part segmentation technique. We derive a simple, approximate algorithm to decide the extrinsic parameters of a virtual camera, and synthesize the turntable image collection of the person using the IBVH algorithm by actively moving the virtual camera on a properly computed circular trajectory around the person. Using the turning function distance as the silhouette similarity measurement, this approach can be used to generate the desired pose-normalized images for recognition applications. In order to overcome the inability of the visual hull (VH) method to reconstruct concave regions, we propose a contour-based human body part localization algorithm to segment the silhouette images into convex body parts. The body parts observed from the virtual view are generated separately from the corresponding body parts observed from the input views and then assembled together for a more accurate VH reconstruction. Furthermore, the obtained turntable image collection helps to improve the body part segmentation and identification process. By using the inner distance shape context (IDSC) measurement, we are able to estimate the body part locations more accurately from a synthesized view where we can localize the body part more precisely. Experiments show that the proposed algorithm can greatly improve body part segmentation and hence shape reconstruction results.