K. Bartol et al., "A Review of 3D Human Pose Estimation from 2D Images", Proc. of 3DBODY.TECH 2020 - 11th Int. Conf. and Exh. on 3D Body Scanning and Processing Technologies, Online/Virtual, 17-18 Nov. 2020, #29, https://doi.org/10.15221/20.29.

Title:

A Review of 3D Human Pose Estimation from 2D Images

Authors:

Kristijan BARTOL 1, David BOJANIC 1, Tomislav PETKOVIC 1, Nicola D'APUZZO 2, Tomislav PRIBANIC 1

1 University of Zagreb, Faculty of Electrical Engineering and Computing, Croatia;
2 Hometrica Consulting, Ascona, Switzerland

Abstract:

Human pose estimation task takes images as input and extracts a set of locations representing the predefined body joints and the sparse connections between the joints, called the body parts. A pose can be estimated from single or multiple frames, in a single (monocular) or multi-view (stereo) setup and for a single person or multiple people in the scene. In this work, we provide an overview of the classic and deep learning-based 3D pose estimation approaches. We also point out relevant evaluation metrics, pose parametrizations, body models, and 3D human pose datasets. Finally, we review state-of-the-art pose estimation results, briefly discuss open problems, and propose possible future research directions.

Keywords:

3d computer vision, human pose estimation, review

Details:

Full paper: 2029bartol.pdf
Proceedings: 3DBODY.TECH 2020, 17-18 Nov. 2020, Online/Virtual
Paper id#: 29
DOI: 10.15221/20.29
Presentation video: 2029bartol.mp4

Copyright notice

© Hometrica Consulting - Dr. Nicola D'Apuzzo, Switzerland, www.hometrica.ch.
Reproduction of the proceedings or any parts thereof (excluding short quotations for the use in the preparation of reviews and technical and scientific papers) may be made only after obtaining the specific approval of the publisher. The papers appearing in the proceedings reflect the author's opinions. Their inclusion in these publications does not necessary constitute endorsement by the editor or by the publisher. Authors retain all rights to individual papers.

Note: click the + on the top left of the page to open/close the menu.