  This work studies the multi-human parsing problem. Existing methods, either
following top-down or bottom-up two-stage paradigms, usually involve expensive
computational costs. We instead present a high-performance Single-stage
Multi-human Parsing (SMP) deep architecture that decouples the multi-human
parsing problem into two fine-grained sub-problems, i.e., locating the human
body and parts. SMP leverages the point features in the barycenter positions to
obtain their segmentation and then generates a series of offsets from the
barycenter of the human body to the barycenters of parts, thus performing human
body and parts matching without the grouping process. Within the SMP
architecture, we propose a Refined Feature Retain module to extract the global
feature of instances through generated mask attention and a Mask of Interest
Reclassify module as a trainable plug-in module to refine the classification
results with the predicted segmentation. Extensive experiments on the MHPv2.0
dataset demonstrate the best effectiveness and efficiency of the proposed
method, surpassing the state-of-the-art method by 2.1% in AP50p, 1.0% in
APvolp, and 1.2% in PCP50. In particular, the proposed method requires fewer
training epochs and a less complex model architecture. We will release our
source codes, pretrained models, and online demos to facilitate further
studies.
