Human pose estimation using deep learning: review, methodologies, progress and future research directions

Human pose estimation (HPE) has developed over the past decade into a vibrant field for research with a variety of real-world applications like 3D reconstruction, virtual testing and re-identification of the person. Information about human poses is also a critical component in many downstream tasks, such as activity recognition and movement tracking. This review focuses on the key aspects of deep learning in the development of both 2D & 3D HPE. It provides detailed information on the variety of databases, performance metrics and human body models incorporated for implementing HPE methodologies. This paper discusses variety of applications of HPE across domains like activity recognition, animation and gaming, virtual reality, video tracking, etc. The paper presents an analytical study of all the major works that use deep learning methods for various downstream tasks in each domain for both 2D & 3D HPE. Finally, it discusses issues and limitations in the current topic of HPE and recommend potential future research directions in order to make meaningful progress in this area.

Automated summary

This paper reviews the key aspects of deep learning in the development of 2D and 3D human pose estimation (HPE), including databases, performance metrics, and body models. It discusses the various applications of HPE in different domains and analyzes the use of deep learning methods for downstream tasks. Furthermore, it addresses the limitations and issues in the current HPE research and recommends potential future directions.