Abstract
Since the last years and until now, technology has made fast progress for many industries, in particularly, garment industry which aims to follow consumer desires and demands. One of these demands is to fit clothes before purchasing them on-line. Therefore, many research works have been focused on how to develop an intelligent apparel industry to ensure the online shopping experience. Image-based virtual try-on is among the most potential approach of virtual fitting that tries on target clothes into customer’s image, therefore, it has received considerable research efforts in the recent years. However, there are several challenges involved in development of virtual try-on that make it difficult to achieve naturally looking virtual outfit such as shape, pose, occlusion, illumination cloth texture, logo and text etc. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of virtual try-on. This review first introduces virtual try-on and its challenges followed by its demand in fashion industry. We summarize state-of-the-art image based virtual try-on for both fashion detection and fashion synthesis as well as their respective advantages, drawbacks, and guidelines for selection of specific try-on model followed by its recent development and successful application. Finally, we conclude the paper with promising directions for future research.
Highlights
In the last few years and especially during COVID-19 pandemic, online shopping for clothes has become a common practice among millions of people around the world
Han et al [16] proposed a two-stage pipeline called VIrtual Try-On Network (VITON) to transfer desired in-shop clothing onto a consumer’s body by allowing the first stage to warp the input item to the desired deformation style and enabling the second stage to align the warped clothes to the consumer’s image. Many approaches following this pipeline have been proposed with more competitive performance such as CPVTON [84] and CP-VTON+ [50], which adopt a thinplate spline (TPS) transformation learnable [9] based on Convolutional neural network architecture for geometric matching to align explicitly input clothing with body shape. All these works are powered by the use of TPS, in the following Figure (Fig. 9) we present its application on VITON architecture [16]
The advancements made with Artificial Intelligence (AI) technologies in fashion industry have not yet reach the goal of modeling the real-world problems which is still very limited and remain challenging, and this is because important hurdles exist at various levels
Summary
In the last few years and especially during COVID-19 pandemic, online shopping for clothes has become a common practice among millions of people around the world It shows a great progress and become a habitual activity for many consumers. In 2012, Converse was the first brand that used virtual iPhone try-on by allowing their clients to use phone cameras to see how shoes looked on them, and post photos on social media as well as make online purchases [92] This technology applies very well to shoes, apparel, accessories, jewelry as well as make-up, where consumers long for a sense of “touch and feel” and they have total freedom regarding decision making, trying, and choosing products at their own pace, without feeling the pressure to make a purchase. In 2020, a revenue of 718 billion US dollars area attained in the fashion sector and an expectation to reach a growth of more than 8.4% for coming years [73]
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