Fit Moi: Online Virtual Fitting Room With Texture Identification And Recommendation System

AbdElminaam, Diaa; mohamed, yasmin; osama, salma; Michelle, rogina; Karam, youssef; Hassan, Mennat Allah

doi:10.21608/jocc.2023.307055

Cited by 1 publication

(1 citation statement)

References 34 publications

(46 reference statements)

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“…Omkar et al [29] in 2023 have combined AR with DL to build a VFR. The authors confirmed that their approach that renders the image cloth on the customer's image improved the customer's experience, but they also mentioned nothing factual about their results.…”

Section: Literature Reviewmentioning

confidence: 99%

A Cost-Efficient Approach for Creating Virtual Fitting Room using Generative Adversarial Networks (GANs)

Attallah,

Zaky,

Abdelrhim

et al. 2024

IJACSA

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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.

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Section: Literature Reviewmentioning

confidence: 99%