  Human parsing has received considerable interest due to its wide application
potentials. Nevertheless, it is still unclear how to develop an accurate human
parsing system in an efficient and elegant way. In this paper, we identify
several useful properties, including feature resolution, global context
information and edge details, and perform rigorous analyses to reveal how to
leverage them to benefit the human parsing task. The advantages of these useful
properties finally result in a simple yet effective Context Embedding with Edge
Perceiving (CE2P) framework for single human parsing. Our CE2P is end-to-end
trainable and can be easily adopted for conducting multiple human parsing.
Benefiting the superiority of CE2P, we achieved the 1st places on all three
human parsing benchmarks. Without any bells and whistles, we achieved 56.50\%
(mIoU), 45.31\% (mean $AP^r$) and 33.34\% ($AP^p_{0.5}$) in LIP, CIHP and MHP
v2.0, which outperform the state-of-the-arts more than 2.06\%, 3.81\% and
1.87\%, respectively. We hope our CE2P will serve as a solid baseline and help
ease future research in single/multiple human parsing. Code has been made
available at \url{https://github.com/liutinglt/CE2P}.
