  Most state-of-the-art instance-level human parsing models adopt two-stage
anchor-based detectors and, therefore, cannot avoid the heuristic anchor box
design and the lack of analysis on a pixel level. To address these two issues,
we have designed an instance-level human parsing network which is anchor-free
and solvable on a pixel level. It consists of two simple sub-networks: an
anchor-free detection head for bounding box predictions and an edge-guided
parsing head for human segmentation. The anchor-free detector head inherits the
pixel-like merits and effectively avoids the sensitivity of hyper-parameters as
proved in object detection applications. By introducing the part-aware boundary
clue, the edge-guided parsing head is capable to distinguish adjacent human
parts from among each other up to 58 parts in a single human instance, even
overlapping instances. Meanwhile, a refinement head integrating box-level score
and part-level parsing quality is exploited to improve the quality of the
parsing results. Experiments on two multiple human parsing datasets (i.e., CIHP
and LV-MHP-v2.0) and one video instance-level human parsing dataset (i.e., VIP)
show that our method achieves the best global-level and instance-level
performance over state-of-the-art one-stage top-down alternatives.
