  Human pose estimation (HPE) has attracted a significant amount of attention
from the computer vision community in the past decades. Moreover, HPE has been
applied to various domains, such as human-computer interaction, sports
analysis, and human tracking via images and videos. Recently, deep
learning-based approaches have shown state-of-the-art performance in HPE-based
applications. Although deep learning-based approaches have achieved remarkable
performance in HPE, a comprehensive review of deep learning-based HPE methods
remains lacking in the literature. In this article, we provide an up-to-date
and in-depth overview of the deep learning approaches in vision-based HPE. We
summarize these methods of 2-D and 3-D HPE, and their applications, discuss the
challenges and the research trends through bibliometrics, and provide
insightful recommendations for future research. This article provides a
meaningful overview as introductory material for beginners to deep
learning-based HPE, as well as supplementary material for advanced researchers.
