Review
Version 1
Preserved in Portico This version is not peer-reviewed
A Systematic Review of Recent Deep Learning Approaches for 3D Human Pose Estimation
Version 1
: Received: 1 November 2023 / Approved: 2 November 2023 / Online: 3 November 2023 (03:48:59 CET)
A peer-reviewed article of this Preprint also exists.
El Kaid, A.; Baïna, K. A Systematic Review of Recent Deep Learning Approaches for 3D Human Pose Estimation. J. Imaging 2023, 9, 275. El Kaid, A.; Baïna, K. A Systematic Review of Recent Deep Learning Approaches for 3D Human Pose Estimation. J. Imaging 2023, 9, 275.
Abstract
3D human pose estimation has made significant advancements through the integration of deep learning techniques. This survey provides a comprehensive review of recent 3D human pose estimation methods, with a focus on monocular images, videos, and multi-view cameras.
Our approach stands out through a systematic literature review methodology, ensuring an up-to-date and meticulous overview. Unlike many existing surveys that categorize approaches based on learning paradigms, our survey offers a fresh perspective, delving deeper into the subject.
For image-based approaches, we not only follow existing categorizations but also introduce and compare significant 2D models. Additionally, we provide a comparative analysis of these methods, enhancing the understanding of image-based pose estimation techniques. In the realm of video-based approaches, we categorize them based on the types of models used to capture inter-frame information.
Furthermore, in the context of multi-person pose estimation, our survey uniquely differentiates between approaches focusing on relative poses and those addressing absolute poses.
Our survey aims to serve as a pivotal resource for researchers, highlighting state-of-the-art deep learning strategies and identifying promising directions for future exploration in 3D human pose estimation.
Keywords
3D human pose estimation; systematic literature survey; deep learning based methods
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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