Absolute Human Pose Estimation with Depth Prediction Network

Marton Veges, Andras Lorincz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2019 International Joint Conference on Neural Networks, IJCNN 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728119854
DOIs
Publication statusPublished - Jul 2019
Event2019 International Joint Conference on Neural Networks, IJCNN 2019 - Budapest, Hungary
Duration: Jul 14 2019Jul 19 2019

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2019-July

Conference

Conference2019 International Joint Conference on Neural Networks, IJCNN 2019
CountryHungary
CityBudapest
Period7/14/197/19/19

Keywords

  • absolute pose estimation
  • depth prediction
  • global coordinates
  • human pose estimation

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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  • Cite this

    Veges, M., & Lorincz, A. (2019). Absolute Human Pose Estimation with Depth Prediction Network. In 2019 International Joint Conference on Neural Networks, IJCNN 2019 [8852387] (Proceedings of the International Joint Conference on Neural Networks; Vol. 2019-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2019.8852387