Absolute Human Pose Estimation with Depth Prediction Network
The common approach to 3D human pose estimation is predicting the body joint coordinates relative to the hip. This works well for a single person but is insufficient in the case of multiple interacting people. Methods predicting absolute coordinates first estimate a root-relative pose then calculate the translation via a secondary optimization task. We propose a neural network that predicts joints in a camera centered coordinate system instead of a root-relative one. Unlike previous methods, our network works in a single step without any post-processing. Our network beats previous methods on the MuPoTS-3D dataset and achieves state-of-the-art results.
PDF AbstractCode
MegaDepth
MuPoTS-3D
MuCo-3DHP
Results from the Paper
Ranked #1 on
3D Multi-Person Pose Estimation (absolute)
on MuPoTS-3D
(MPJPE metric)
