Monocular Human Pose Estimation: A Survey of Deep Learning-based Methods
It provides a comprehensive overview for researchers in computer vision, but is incremental as it synthesizes existing work without new results.
This survey reviews deep learning-based methods for monocular human pose estimation from 2D and 3D images since 2014, summarizing challenges, frameworks, datasets, metrics, and performance comparisons.
Vision-based monocular human pose estimation, as one of the most fundamental and challenging problems in computer vision, aims to obtain posture of the human body from input images or video sequences. The recent developments of deep learning techniques have been brought significant progress and remarkable breakthroughs in the field of human pose estimation. This survey extensively reviews the recent deep learning-based 2D and 3D human pose estimation methods published since 2014. This paper summarizes the challenges, main frameworks, benchmark datasets, evaluation metrics, performance comparison, and discusses some promising future research directions.