  Existing methods of multiple human parsing usually adopt a two-stage strategy
(typically top-down and bottom-up), which suffers from either strong dependence
on prior detection or highly computational redundancy during post-grouping. In
this work, we present an end-to-end multiple human parsing framework using
representative parts, termed RepParser. Different from mainstream methods,
RepParser solves the multiple human parsing in a new single-stage manner
without resorting to person detection or post-grouping.To this end, RepParser
decouples the parsing pipeline into instance-aware kernel generation and
part-aware human parsing, which are responsible for instance separation and
instance-specific part segmentation, respectively. In particular, we empower
the parsing pipeline by representative parts, since they are characterized by
instance-aware keypoints and can be utilized to dynamically parse each person
instance. Specifically, representative parts are obtained by jointly localizing
centers of instances and estimating keypoints of body part regions. After that,
we dynamically predict instance-aware convolution kernels through
representative parts, thus encoding person-part context into each kernel
responsible for casting an image feature as an instance-specific
representation.Furthermore, a multi-branch structure is adopted to divide each
instance-specific representation into several part-aware representations for
separate part segmentation.In this way, RepParser accordingly focuses on person
instances with the guidance of representative parts and directly outputs
parsing results for each person instance, thus eliminating the requirement of
the prior detection or post-grouping.Extensive experiments on two challenging
benchmarks demonstrate that our proposed RepParser is a simple yet effective
framework and achieves very competitive performance.
