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Image virtual try-on aims to fit a garment image (target clothes) to a personimage. Prior methods are heavily based on human parsing. However,slightly-wrong segmentation results would lead to unrealistic try-on imageswith large artifacts. Inaccurate parsing misleads parser-based methods toproduce visually unrealistic results where artifacts usually occur. A recentpioneering work employed knowledge distillation to reduce the dependency ofhuman parsing, where the try-on images produced by a parser-based method areused as supervisions to train a "student" network without relying onsegmentation, making the student mimic the try-on ability of the parser-basedmodel. However, the image quality of the student is bounded by the parser-basedmodel. To address this problem, we propose a novel approach,"teacher-tutor-student" knowledge distillation, which is able to produce highlyphoto-realistic images without human parsing, possessing several appealingadvantages compared to prior arts. (1) Unlike existing work, our approachtreats the fake images produced by the parser-based method as "tutorknowledge", where the artifacts can be corrected by real "teacher knowledge",which is extracted from the real person images in a self-supervised way. (2)Other than using real images as supervisions, we formulate knowledgedistillation in the try-on problem as distilling the appearance flows betweenthe person image and the garment image, enabling us to find accurate densecorrespondences between them to produce high-quality results. (3) Extensiveevaluations show large superiority of our method (see Fig. 1).

Parser-Free Virtual Try-on via Distilling Appearance Flows