A Review of 3D Human Pose Estimation from 2D Images - 20.29
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
Full paper Abstract:
Presentation:
Details:
Proceedings: 3DBODY.TECH 2020, 17-18 Nov. 2020, Online/Virtual
Paper id#: 29
DOI: 10.15221/20.29
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