Deep 3D Human Pose Estimation Under Partial Body Presence

This paper addresses the problem of 3D human pose estimation when not all body parts are present in the input image, i.e., when some body joints are present while other joints are fully absent (we exclude self-occlusion). State-of-the-art is not designed and thus not effective for such cases. We propose a deep CNN to regress the human pose directly from an input image; we design and train this network to work under partial body presence. Parallel to this, we train a detection network to classify the presence or absence of each of the main body joints in the input image. The outputs of our detection and regression networks are a) joints that are present and b) joints that are absent. With these outputs, our method reconstructs the full body skeleton. Evaluations on the Hu-man3.6M dataset yield promising results compared to related work.