JointFusionNet: Parallel Learning Human Structural Local and Global Joint Features for 3D Human Pose Estimation

Zhiwei Yuan, Yaping Yan, Songlin Du*, Takeshi Ikenaga

*Corresponding author for this work

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

Abstract

3D human pose estimation plays important roles in various human-machine interactive applications, but how to efficiently utilize the joint structural global and local features of human pose in deep-learning-based methods has always been a challenge. In this paper, we propose a parallel structural global and local joint features fusion network based on inspiring observation pattern of human pose. To be specific, it is observed that there are common similar global features and local features in human pose cross actions. Therefore, we design global-local capture modules separately to capture features and finally fuse them. The proposed parallel global and local joint features fusion network, entitled JointFusionNet, significantly improve state-of-the-art models on both intra-scenario H36M and cross-scenario 3DPW datasets and lead to appreciable improvements in poses with more similar local features. Notably, it yields an overall improvement of 3.4 mm in MPJPE (relative 6.8 % improvement) over the previous best feature fusion based method [22] on H36M dataset in 3D human pose estimation.

Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning - ICANN 2022 - 31st International Conference on Artificial Neural Networks, Proceedings
EditorsElias Pimenidis, Mehmet Aydin, Plamen Angelov, Chrisina Jayne, Antonios Papaleonidas
PublisherSpringer Science and Business Media Deutschland GmbH
Pages113-125
Number of pages13
ISBN (Print)9783031159367
DOIs
Publication statusPublished - 2022
Event31st International Conference on Artificial Neural Networks, ICANN 2022 - Bristol, United Kingdom
Duration: 2022 Sep 62022 Sep 9

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13532 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference31st International Conference on Artificial Neural Networks, ICANN 2022
Country/TerritoryUnited Kingdom
CityBristol
Period22/9/622/9/9

Keywords

  • 3D human pose estimation
  • Feature fusion
  • Human structural joint features

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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