Publication

Abstract : 3D human pose estimation is integral to applications, including activity recognition, animation generation, and performance analysis. Deep learning techniques have greatly advanced the field of 3D human pose estimation, allowing for more precise and accurate results. It is crucial that in spite of these advancements, there are still limitations on how these techniques can be used in real-life situations. Accurately calculating poses for multiple subjects or in challenging situations, such as outdoor environments, rapidly evolving scenarios, or circumstances where the subject is too small or too far away from the camera, remains a challenge. These issues are especially noticeable when using monocular camera setups, which might make it more difficult to get precise results. This systematic review synthesizes recent trends in monocular 3D pose estimation, detailing tools, technologies, approaches, and strategies used in the past decade. Out of 432 initial publications, 103 peer-reviewed papers from scholarly databases were selected, offering insights into the field's progression and current trends. The study details the evolution of methodologies, with a focus on regression and detection-based methods, and discusses prevalent datasets and performance metrics. The findings emphasize the need to address challenges like depth ambiguity and occlusions for real-world applicability. This comprehensive review aims to understand the trajectory and prospects of 3D Human Pose Estimation, with implications for diverse applications.