  Human parsing is attracting increasing research attention. In this work, we
aim to push the frontier of human parsing by introducing the problem of
multi-human parsing in the wild. Existing works on human parsing mainly tackle
single-person scenarios, which deviates from real-world applications where
multiple persons are present simultaneously with interaction and occlusion. To
address the multi-human parsing problem, we introduce a new multi-human parsing
(MHP) dataset and a novel multi-human parsing model named MH-Parser. The MHP
dataset contains multiple persons captured in real-world scenes with
pixel-level fine-grained semantic annotations in an instance-aware setting. The
MH-Parser generates global parsing maps and person instance masks
simultaneously in a bottom-up fashion with the help of a new Graph-GAN model.
We envision that the MHP dataset will serve as a valuable data resource to
develop new multi-human parsing models, and the MH-Parser offers a strong
baseline to drive future research for multi-human parsing in the wild.
