Recent Advances in 3D Human Pose Estimation: From Optimization to Implementation and beyond

Jielu YAN, Mingliang ZHOU*, Jinli PAN, Meng YIN, Bin FANG

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

7 Citations (Scopus)

Abstract

3D human pose estimation describes estimating 3D articulation structure of a person from an image or a video. The technology has massive potential because it can enable tracking people and analyzing motion in real time. Recently, much research has been conducted to optimize human pose estimation, but few works have focused on reviewing 3D human pose estimation. In this paper, we offer a comprehensive survey of the state-of-the-art methods for 3D human pose estimation, referred to as pose estimation solutions, implementations on images or videos that contain different numbers of people and advanced 3D human pose estimation techniques. Furthermore, different kinds of algorithms are further subdivided into sub-categories and compared in light of different methodologies. To the best of our knowledge, this is the first such comprehensive survey of the recent progress of 3D human pose estimation and will hopefully facilitate the completion, refinement and applications of 3D human pose estimation.

Original languageEnglish
Article number2255003
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Volume36
Issue number2
Early online date12 Jan 2022
DOIs
Publication statusPublished - 1 Feb 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2022 World Scientific Publishing Company.

Keywords

  • 3D human pose estimation
  • deep learning
  • multi-person
  • pictorial structure
  • unsupervised

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