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Absolute Human Pose Estimation with Depth Prediction Network
lib:3d03abc612d423da (v1.0.0)
Authors: Márton Véges,András Lőrincz
ArXiv: 1904.05947
Document: PDF DOI
Artifact development version: GitHub
Abstract URL: http://arxiv.org/abs/1904.05947v1
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.
ArXiv: 1904.05947
Document: PDF DOI
Artifact development version: GitHub
Abstract URL: http://arxiv.org/abs/1904.05947v1
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.
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