  Estimation of the human pose from a monocular camera has been an emerging
research topic in the computer vision community with many applications.
Recently, benefited from the deep learning technologies, a significant amount
of research efforts have greatly advanced the monocular human pose estimation
both in 2D and 3D areas. Although there have been some works to summarize the
different approaches, it still remains challenging for researchers to have an
in-depth view of how these approaches work. In this paper, we provide a
comprehensive and holistic 2D-to-3D perspective to tackle this problem. We
categorize the mainstream and milestone approaches since the year 2014 under
unified frameworks. By systematically summarizing the differences and
connections between these approaches, we further analyze the solutions for
challenging cases, such as the lack of data, the inherent ambiguity between 2D
and 3D, and the complex multi-person scenarios. We also summarize the pose
representation styles, benchmarks, evaluation metrics, and the quantitative
performance of popular approaches. Finally, we discuss the challenges and give
deep thinking of promising directions for future research. We believe this
survey will provide the readers with a deep and insightful understanding of
monocular human pose estimation.
