  The 2D virtual try-on task has recently attracted a great interest from the
research community, for its direct potential applications in online shopping as
well as for its inherent and non-addressed scientific challenges. This task
requires fitting an in-shop cloth image on the image of a person, which is
highly challenging because it involves cloth warping, image compositing, and
synthesizing. Casting virtual try-on into a supervised task faces a difficulty:
available datasets are composed of pairs of pictures (cloth, person wearing the
cloth). Thus, we have no access to ground-truth when the cloth on the person
changes. State-of-the-art models solve this by masking the cloth information on
the person with both a human parser and a pose estimator. Then, image synthesis
modules are trained to reconstruct the person image from the masked person
image and the cloth image. This procedure has several caveats: firstly, human
parsers are prone to errors; secondly, it is a costly pre-processing step,
which also has to be applied at inference time; finally, it makes the task
harder than it is since the mask covers information that should be kept such as
hands or accessories. In this paper, we propose a novel student-teacher
paradigm where the teacher is trained in the standard way (reconstruction)
before guiding the student to focus on the initial task (changing the cloth).
The student additionally learns from an adversarial loss, which pushes it to
follow the distribution of the real images. Consequently, the student exploits
information that is masked to the teacher. A student trained without the
adversarial loss would not use this information. Also, getting rid of both
human parser and pose estimator at inference time allows obtaining a real-time
virtual try-on.
