Cross-View Self-fusion for Self-supervised 3D Human Pose Estimation in the Wild

Hyun Woo Kim, Gun Hee Lee, Myeong Seok Oh, Seong Whan Lee

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


Human pose estimation methods have recently shown remarkable results with supervised learning that requires large amounts of labeled training data. However, such training data for various human activities does not exist since 3D annotations are acquired with traditional motion capture systems that usually require a controlled indoor environment. To address this issue, we propose a self-supervised approach that learns a monocular 3D human pose estimator from unlabeled multi-view images by using multi-view consistency constraints. Furthermore, we refine inaccurate 2D poses, which adversely affect 3D pose predictions, using the property of canonical space without relying on camera calibration. Since we do not require camera calibrations to leverage the multi-view information, we can train a network from in-the-wild environments. The key idea is to fuse the 2D observations across views and combine predictions from the observations to satisfy the multi-view consistency during training. We outperform state-of-the-art methods in self-supervised learning on the two benchmark datasets Human3.6M and MPI-INF-3DHP as well as on the in-the-wild dataset SkiPose. Code and models are available at

Original languageEnglish
Title of host publicationComputer Vision – ACCV 2022 - 16th Asian Conference on Computer Vision, 2022, Proceedings
EditorsLei Wang, Juergen Gall, Tat-Jun Chin, Imari Sato, Rama Chellappa
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages18
ISBN (Print)9783031263187
Publication statusPublished - 2023
Event16th Asian Conference on Computer Vision, ACCV 2022 - Macao, China
Duration: 2022 Dec 42022 Dec 8

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13841 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th Asian Conference on Computer Vision, ACCV 2022

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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