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The common approach to 3D human pose estimation is predicting the body jointcoordinates relative to the hip. This works well for a single person but isinsufficient in the case of multiple interacting people. Methods predictingabsolute coordinates first estimate a root-relative pose then calculate thetranslation via a secondary optimization task. We propose a neural network thatpredicts joints in a camera centered coordinate system instead of aroot-relative one. Unlike previous methods, our network works in a single stepwithout any post-processing. Our network beats previous methods on theMuPoTS-3D dataset and achieves state-of-the-art results.

Absolute Human Pose Estimation with Depth Prediction Network