Toward Accurate Human Parsing Through Edge Guided Diffusion.

Existing human parsing frameworks commonly employ joint learning of semantic edge detection and human parsing to facilitate the localization around boundary regions. Nevertheless, the parsing prediction within the interior of the part contour may still exhibit inconsistencies due to the inherent ambiguity of fine-grained semantics. In contrast, binary edge detection does not suffer from such fine-grained semantic ambiguity, leading to a typical failure case where misclassification occurs inner the part contour while the semantic edge is accurately detected. To address these challenges, we develop a novel diffusion scheme that incorporates guidance from the detected semantic edge to mitigate this problem by propagating corrected classified semantics into the misclassified regions. Building upon this diffusion scheme, we present an Edge Guided Diffusion Network (EGDNet) for human parsing, which can progressively refine the parsing predictions to enhance the accuracy and coherence of human parsing results. Moreover, we design a horizontal-vertical aggregation to exploit inherent correlations among body parts along both the horizontal and vertical axes, which aims at enhancing the initial parsing results. Extensive experimental evaluations on various challenging datasets demonstrate the effectiveness of the proposed EGDNet. Remarkably, our EGDNet shows impressive performances on six benchmark datasets, including four human body parsing datasets (LIP, CIHP, ATR, and PASCAL-Person-Part), and two human face parsing datasets (CelebAMask-HQ and LaPa).