  Human pose estimation aims at localizing human anatomical keypoints or body
parts in the input data (e.g., images, videos, or signals). It forms a crucial
component in enabling machines to have an insightful understanding of the
behaviors of humans, and has become a salient problem in computer vision and
related fields. Deep learning techniques allow learning feature representations
directly from the data, significantly pushing the performance boundary of human
pose estimation. In this paper, we reap the recent achievements of 2D human
pose estimation methods and present a comprehensive survey. Briefly, existing
approaches put their efforts in three directions, namely network architecture
design, network training refinement, and post processing. Network architecture
design looks at the architecture of human pose estimation models, extracting
more robust features for keypoint recognition and localization. Network
training refinement tap into the training of neural networks and aims to
improve the representational ability of models. Post processing further
incorporates model-agnostic polishing strategies to improve the performance of
keypoint detection. More than 200 research contributions are involved in this
survey, covering methodological frameworks, common benchmark datasets,
evaluation metrics, and performance comparisons. We seek to provide researchers
with a more comprehensive and systematic review on human pose estimation,
allowing them to acquire a grand panorama and better identify future
directions.
