A Systematic Review of Recent Deep Learning Approaches for 3D Human Pose Estimation

• Published in: Journal of imaging Vol. 9; no. 12; p. 275
• Main Authors: El Kaid, Amal, Baïna, Karim
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.12.2023
• Subjects: 3D human pose estimation; Body, Human; Deep learning; deep-learning-based methods; Image enhancement; Image processing; Literature reviews; Machine learning; Methods; Physiological research; Pose estimation; Posture; systematic literature survey; Systematic review; Taxonomy; Two dimensional models; Morocco; systematic literature survey; deep-learning-based methods; 3D human pose estimation
• ISSN: 2313-433X; 2313-433X
• DOI: 10.3390/jimaging9120275

Three-dimensional 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.