  Human parsing aims to partition humans in image or video into multiple
pixel-level semantic parts. In the last decade, it has gained significantly
increased interest in the computer vision community and has been utilized in a
broad range of practical applications, from security monitoring, to social
media, to visual special effects, just to name a few. Although deep
learning-based human parsing solutions have made remarkable achievements, many
important concepts, existing challenges, and potential research directions are
still confusing. In this survey, we comprehensively review three core
sub-tasks: single human parsing, multiple human parsing, and video human
parsing, by introducing their respective task settings, background concepts,
relevant problems and applications, representative literature, and datasets. We
also present quantitative performance comparisons of the reviewed methods on
benchmark datasets. Additionally, to promote sustainable development of the
community, we put forward a transformer-based human parsing framework,
providing a high-performance baseline for follow-up research through universal,
concise, and extensible solutions. Finally, we point out a set of
under-investigated open issues in this field and suggest new directions for
future study. We also provide a regularly updated project page, to continuously
track recent developments in this fast-advancing field:
https://github.com/soeaver/awesome-human-parsing.
