A survey on deep learning for 2D and 3D human pose estimation

Human pose estimation is a fundamental task in computer vision and robotics that involves detecting the human body joints from images or videos. It became a rapidly evolving field with applications ranging from action recognition to healthcare. This survey provides a detailed review of various methods in 2D and 3D human pose estimation for single-person and multi-person contexts in both image-based and video-based scenarios. We present a comprehensive categorization and comparison of available 2D and 3D pose datasets with an emphasis on their strengths and limitations. In addition, we also provide an overview of various evaluation metrics and loss functions commonly used to evaluate the accuracy and robustness of pose estimation models. We further discuss emerging trends, offering readers an insight into current trends in the field. We then explore key application domains where pose estimation plays an important role. The survey explains in detail about challenges in human pose estimation, including occlusion, data scarcity, privacy concerns, generalization issues, and model complexity, and suggests potential future research directions. Overall, this review aims to guide researchers in understanding current methods, datasets, and applications, while pointing out open issues and highlighting the future scope of human pose estimation.