POSERN: A 2D POSE REFINEMENT NETWORK FOR BIAS-FREE MULTI-VIEW 3D HUMAN POSE ESTIMATION

Akihiko Sayo, Diego Thomas, Hiroshi Kawasaki, Yuta Nakashima, Katsushi Ikeuchi

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

Abstract

We propose a new 2D pose refinement network that learns to predict the human bias in the estimated 2D pose. There are biases in 2D pose estimations that are due to differences between annotations of 2D joint locations based on annotators’ perception and those defined by motion capture (MoCap) systems. These biases are crafted into publicly available 2D pose datasets and cannot be removed with existing error reduction approaches. Our proposed pose refinement network allows us to efficiently remove the human bias in the estimated 2D poses and achieve highly accurate multi-view 3D human pose estimation.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Image Processing, ICIP 2021 - Proceedings
PublisherIEEE Computer Society
Pages3233-3237
Number of pages5
ISBN (Electronic)9781665441155
DOIs
Publication statusPublished - 2021
Event2021 IEEE International Conference on Image Processing, ICIP 2021 - Anchorage, United States
Duration: Sep 19 2021Sep 22 2021

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2021-September
ISSN (Print)1522-4880

Conference

Conference2021 IEEE International Conference on Image Processing, ICIP 2021
Country/TerritoryUnited States
CityAnchorage
Period9/19/219/22/21

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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