Self-occlusion and 3D pose estimation in still images

In this paper we propose a self-occlusion and 3D pose estimation model for human figures in still images based on a user-provided 2D skeleton. An initial segmentation model is used to capture labeled human body parts in a 2D image. Then, occluded body parts are detected when different body parts overlap, and are disambiguated by analyzing the energy of the corresponding contours around the intersection points. The estimated occlusion results feed the 3D pose estimation algorithm, which reconstructs a set of plausible 3D postures. Experimental results indicate that the proposed technique works well in non trivial images, effectively estimating the occluded body parts and reducing the number of possible 3D postures.