  Image virtual try-on aims to fit a garment image (target clothes) to a person
image. Prior methods are heavily based on human parsing. However,
slightly-wrong segmentation results would lead to unrealistic try-on images
with large artifacts. Inaccurate parsing misleads parser-based methods to
produce visually unrealistic results where artifacts usually occur. A recent
pioneering work employed knowledge distillation to reduce the dependency of
human parsing, where the try-on images produced by a parser-based method are
used as supervisions to train a "student" network without relying on
segmentation, making the student mimic the try-on ability of the parser-based
model. However, the image quality of the student is bounded by the parser-based
model. To address this problem, we propose a novel approach,
"teacher-tutor-student" knowledge distillation, which is able to produce highly
photo-realistic images without human parsing, possessing several appealing
advantages compared to prior arts. (1) Unlike existing work, our approach
treats the fake images produced by the parser-based method as "tutor
knowledge", 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 knowledge
distillation in the try-on problem as distilling the appearance flows between
the person image and the garment image, enabling us to find accurate dense
correspondences between them to produce high-quality results. (3) Extensive
evaluations show large superiority of our method (see Fig. 1).
