  Human pose estimation in unconstrained images and videos is a fundamental
computer vision task. To illustrate the evolutionary path in technique, in this
survey we summarize representative human pose methods in a structured taxonomy,
with a particular focus on deep learning models and single-person image
setting. Specifically, we examine and survey all the components of a typical
human pose estimation pipeline, including data augmentation, model architecture
and backbone, supervision representation, post-processing, standard datasets,
evaluation metrics. To envisage the future directions, we finally discuss the
key unsolved problems and potential trends for human pose estimation.
