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This study presents a new network (i.e., PoseLifter) that can lift a 2D humanpose to an absolute 3D pose in a camera coordinate system. The proposed networkestimates the absolute 3D location of a target subject and generates animproved 3D relative pose estimation compared with existing pose-liftingmethods. Using the PoseLifter with a 2D pose estimator in a cascade fashion canestimate a 3D human pose from a single RGB image. In this case, we empiricallyprove that using realistic 2D poses synthesized with the real errordistribution of 2D body joints considerably improves the performance of ourPoseLifter. The proposed method is applied to public datasets to achievestate-of-the-art 2D-to-3D pose lifting and 3D human pose estimation.

PoseLifter: Absolute 3D human pose lifting network from a single noisy 2D human pose