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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...
In this paper we propose a skeleton-based model for human segmentation in static images. Our approach explores edge information, orientation coherence and anthropometric-estimated parameters to generate a graph, and the desired contour is a path with maximal cost. Experimental results show that the proposed technique works well in non trivial images.
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