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Virtual try-on(VTON) aims at fitting target clothes to reference personimages, which is widely adopted in e-commerce.Existing VTON approaches can benarrowly categorized into Parser-Based(PB) and Parser-Free(PF) by whetherrelying on the parser information to mask the persons' clothes and synthesizetry-on images. Although abandoning parser information has improved theapplicability of PF methods, the ability of detail synthesizing has also beensacrificed. As a result, the distraction from original cloth may persistinsynthesized images, especially in complicated postures and high resolutionapplications. To address the aforementioned issue, we propose a novel PF methodnamed Regional Mask Guided Network(RMGN). More specifically, a regional mask isproposed to explicitly fuse the features of target clothes and referencepersons so that the persisted distraction can be eliminated. A postureawareness loss and a multi-level feature extractor are further proposed tohandle the complicated postures and synthesize high resolution images.Extensive experiments demonstrate that our proposed RMGN outperforms bothstate-of-the-art PB and PF methods.Ablation studies further verify theeffectiveness ofmodules in RMGN.

RMGN: A Regional Mask Guided Network for Parser-free Virtual Try-on