  This study presents a new network (i.e., PoseLifter) that can lift a 2D human
pose to an absolute 3D pose in a camera coordinate system. The proposed network
estimates the absolute 3D location of a target subject and generates an
improved 3D relative pose estimation compared with existing pose-lifting
methods. Using the PoseLifter with a 2D pose estimator in a cascade fashion can
estimate a 3D human pose from a single RGB image. In this case, we empirically
prove that using realistic 2D poses synthesized with the real error
distribution of 2D body joints considerably improves the performance of our
PoseLifter. The proposed method is applied to public datasets to achieve
state-of-the-art 2D-to-3D pose lifting and 3D human pose estimation.

Computer Vision and Pattern Recognition