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Multi-person 3D human pose estimation from a single image is a challengingproblem, especially for in-the-wild settings due to the lack of 3D annotateddata. We propose HG-RCNN, a Mask-RCNN based network that also leverages thebenefits of the Hourglass architecture for multi-person 3D Human PoseEstimation. A two-staged approach is presented that first estimates the 2Dkeypoints in every Region of Interest (RoI) and then lifts the estimatedkeypoints to 3D. Finally, the estimated 3D poses are placed incamera-coordinates using weak-perspective projection assumption and jointoptimization of focal length and root translations. The result is a simple andmodular network for multi-person 3D human pose estimation that does not requireany multi-person 3D pose dataset. Despite its simple formulation, HG-RCNNachieves the state-of-the-art results on MuPoTS-3D while also approximating the3D pose in the camera-coordinate system.

Multi-Person 3D Human Pose Estimation from Monocular Images