Title: 3D Human Pose Estimation in RGBD Images for Robotic Task Learning,
Abstract: We propose an approach to estimate 3D human pose in real world units from a
single RGBD image and show that it exceeds performance of monocular 3D pose
estimation approaches from color as well as pose estimation exclusively from
depth. Our approach builds on robust human keypoint detectors for color images
and incorporates depth for lifting into 3D. We combine the system with our
learning from demonstration framework to instruct a service robot without the
need of markers. Experiments in real world settings demonstrate that our
approach enables a PR2 robot to imitate manipulation actions observed from a
human teacher.