MONOCULAR 3D HUMAN POSE ESTIMATION BY CLASSIFICATION

We present a novel approach to 2D and 3D human pose estimation in monocular images by building on and improving recent advances in this field. We take the full body pose as a combination of a 3D pose and a viewpoint and in this way define classes that are then learned by a classifier. Compared to part based approaches, our approach does not suffer from self-occluded body parts since such occlusions are characteristic for certain classes and thus are captured during class definition. Moreover, we significantly relax the requirements posed on training data by the fact that we do neither require labeled viewpoints nor background subtracted images, and the carried out action does not need to be cyclic. By combining an efficient classifier with efficient image features, we present a generic and fast way to estimate human poses in images and achieve comparable results to state-of-the art approaches which we demonstrate on a public benchmark.