Unveiling the Landscape of Human Pose Estimation

Abstract This paper presents a comprehensive survey and methodology for deep learning-based solutions in articulated human pose estimation (HPE). Recent advances in deep learning have revolutionized the HPE field, with the capturing system transitioning from multi-modal to a regular color camera and from multi-views to a monocular view, opening up numerous applications. However, the increasing variety of deep network architectures has resulted in a vast literature on the topic, making it challenging to identify commonalities and differences among 1 diverse HPE approaches. Therefore, this paper serves two objectives: firstly, it provides a thorough survey of over 100 research papers published since 2015, focusing on deep learning-based solutions for monocular HPE; secondly, it develops a comprehensive methodology that systematically combines existing works and summarizes a unified framework for the HPE problem and its modular components. Unlike previous surveys, this study places emphasis on methodology development in order to provide betters insights and learning opportunities for researchers in the field of computer vision. The paper also summarizes and discusses the quantitative performance of the reviewed methods on popular datasets, while highlighting the challenges involved, such as occlusion and viewpoint variation. Finally, future research directions, such as incorporating temporal information and 3D pose estimation, along with potential solutions to address the remaining challenges in HPE, are presented.