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Do Not Mask What You Do Not Need to Mask: a Parser-Free Virtual Try-On by Issenhuth, Thibaut; Mary, Jérémie; Calauzènes, Clément is a scholarly article available to read on EtoBox.

What is Do Not Mask What You Do Not Need to Mask: a Parser-Free Virtual Try-On about?

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,

Author

Issenhuth, Thibaut; Mary, Jérémie; Calauzènes, Clément

Published

2020

Language

EN