Designing an AI-Based Virtual Try-On Web Application

Marelli, Davide; Bianco, Simone; Ciocca, Gianluigi

doi:10.3390/s22103832

Cited by 17 publications

(2 citation statements)

References 69 publications

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“…Virtual try-on allows for the seamless exploration of various items such as shoes [34], [196], upper clothing [67], [195], lower clothing [58], [189] and more [98], [127]. Some models even offer the convenience of modifying multiple items in a single image simultaneously [199].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning in Virtual Try-On: A Comprehensive Survey

Islam,

Miron,

Liu

et al. 2024

IEEE Access

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Virtual try-on technology has gained significant importance in the retail industry due to its potential to transform the way customers interact with products and make purchase decisions. It allows users to virtually try on clothing and accessories, providing a realistic representation of how the items would look and fit without the need for physical interaction. The ability to virtually try on products addresses common challenges associated with online shopping, such as uncertainty about fit and style, ultimately enhancing the overall customer experience and satisfaction. As a result, virtual try-on technology has the potential to reduce returns and optimise conversion rates for businesses, making it a valuable tool in the e-commerce landscape. In this paper, we provide a comprehensive review of deep learning based virtual try-on models, focusing on their functionality, technical details, dataset usage, weaknesses, and impact on customer satisfaction. The models are categorised into three main types: image-based, multi-pose, and video virtual try-on models, with detailed examples and technical summaries provided for each category. Additionally, we identify and discuss similarities and differences in these methods. Furthermore, we examine the datasets currently available for building and evaluating virtual try-on models, including the number of images/videos and their resolutions. We present the commonly used methods for both qualitative and quantitative evaluations, comparing synthesised images with previous work and performing quantitative evaluations across various metrics and benchmark datasets. We discuss the weaknesses of current deep learning based virtual try-on models, including challenges in preserving clothing characteristics and textures, the level of accuracy of applying the clothing to the person, and the preservation of facial identities. Additionally, we address dataset bias, particularly the domination of female models, limited diversity in clothing featured, and relatively simple and clean backgrounds in the datasets, which can negatively impact the model's ability to handle challenging situations. Moreover, we explore the impact of virtual try-ons on customer satisfaction, highlighting the benefits that customers can enjoy, which also reduces returns and optimises conversion rates for businesses.INDEX TERMS Virtual try-on (VTO), deep learning, image synthesis, generative adversarial networks (GANs), diffusion models (DMs).

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Section: Introductionmentioning

confidence: 99%

Deep Learning in Virtual Try-On: A Comprehensive Survey

Islam,

Miron,

Liu

et al. 2024

IEEE Access

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“…This technology was developed to be easily used by mobile or web applications. Virtual try-on and facial tracking technology are used to choose makeup products with product visualization and prepurchase information (Marelli et al, 2022). One of the pioneers of this technology is Estee Lauder's "30 Lipstick Shades in 30 Seconds" program.…”

Section: Introductionmentioning

confidence: 99%

Customer Intention to Use AI Technology on Beauty Industry

Pambudi,

Dwinata JS

2023

ajtm

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Artificial intelligence has emerged as one of the biggest disruptive technologies of the new era and will continue to evolve in the future. AI takes an important role in marketing processes, and the use of AI in marketing is massively increasing and can be easily found. Beauty industry is one of the industries where most companies develop their AI technology to attract customers. This innovation utilizes AI technology in providing solutions to problems related to makeup and/or skincare as experienced by the customers. This study aims to analyze the relationship between technology readiness, technology acceptance model and subjective norm as potential factors in encouraging the customer to use AI technology in the beauty industry. The data was collected using a google form questionnaire. The sampling technique for this research was purposive random sampling. A total of 155 responses were collated and only 127 responses are qualified with the criteria ‘ever using AI Technology in the beauty industry’. The results showed that while perceived usefulness had no significant impact on the intention to utilize AI technology, perceived comfort and subjective standards did. Additionally, the perceived advantages and ease of use are affected differently by each element of technology readiness.

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Virtual Makeup Try‐On System Using Cognitive Learning

Sanapala,

Angel Arul Jothi

2024

Cognitive Analytics and Reinforcement Learning

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Designing an AI-Based Virtual Try-On Web Application

Cited by 17 publications

References 69 publications

Deep Learning in Virtual Try-On: A Comprehensive Survey

Deep Learning in Virtual Try-On: A Comprehensive Survey

Customer Intention to Use AI Technology on Beauty Industry

Virtual Makeup Try‐On System Using Cognitive Learning

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