Accurate static pose estimation combining direct regression and geodesic extrema

Human pose estimation in static images has received significant attention recently but the problem remains challenging. Using data acquired from a consumer depth sensor, our method combines a direct regression approach for the estimation of rigid body parts with the extraction of geodesic extrema to find extremities. We show how these approaches are complementary and present a novel approach to combine the results resulting in an improvement over the state-of-the-art. We report and compare our results a new dataset of aligned RGB-D pose sequences which we release as a benchmark for further evaluation.