A Cost-Efficient Approach for Creating Virtual Fitting Room using
  Generative Adversarial Networks (GANs)
Kirolos Attallah Girgis Zaky Nourhan Abdelrhim Kyrillos Botros Amjad Dife Nermin Negied
Customers all over the world want to see how the clothes fit them or not
before purchasing. Therefore, customers by nature prefer brick-and-mortar
clothes shopping so they can try on products before purchasing them. But after
the Pandemic of COVID19 many sellers either shifted to online shopping or
closed their fitting rooms which made the shopping process hesitant and
doubtful. The fact that the clothes may not be suitable for their buyers after
purchase led us to think about using new AI technologies to create an online
platform or a virtual fitting room (VFR) in the form of a mobile application
and a deployed model using a webpage that can be embedded later to any online
store where they can try on any number of cloth items without physically trying
them. Besides, it will save much searching time for their needs. Furthermore,
it will reduce the crowding and headache in the physical shops by applying the
same technology using a special type of mirror that will enable customers to
try on faster. On the other hand, from business owners' perspective, this
project will highly increase their online sales, besides, it will save the
quality of the products by avoiding physical trials issues. The main approach
used in this work is applying Generative Adversarial Networks (GANs) combined
with image processing techniques to generate one output image from two input
images which are the person image and the cloth image. This work achieved
results that outperformed the state-of-the-art approaches found in literature.