  Human parsing has recently attracted a lot of research interests due to its
huge application potentials. However existing datasets have limited number of
images and annotations, and lack the variety of human appearances and the
coverage of challenging cases in unconstrained environment. In this paper, we
introduce a new benchmark "Look into Person (LIP)" that makes a significant
advance in terms of scalability, diversity and difficulty, a contribution that
we feel is crucial for future developments in human-centric analysis. This
comprehensive dataset contains over 50,000 elaborately annotated images with 19
semantic part labels, which are captured from a wider range of viewpoints,
occlusions and background complexity. Given these rich annotations we perform
detailed analyses of the leading human parsing approaches, gaining insights
into the success and failures of these methods. Furthermore, in contrast to the
existing efforts on improving the feature discriminative capability, we solve
human parsing by exploring a novel self-supervised structure-sensitive learning
approach, which imposes human pose structures into parsing results without
resorting to extra supervision (i.e., no need for specifically labeling human
joints in model training). Our self-supervised learning framework can be
injected into any advanced neural networks to help incorporate rich high-level
knowledge regarding human joints from a global perspective and improve the
parsing results. Extensive evaluations on our LIP and the public
PASCAL-Person-Part dataset demonstrate the superiority of our method.
