Virtual Try-On in E-Commerce: Challenges and Future Scope

Abstract

The purpose of the study is to analyze the impact of Virtual Try-On (VTO) technology on the market of e-commerce. The study shows an online retail application with VTO capabilities which was developed in React.js on the frontend, Node.js on the backend, and MongoDB for the database. The used RapidHub API was not an exception since it employs basic 2D compositions that lack realism. AI-driven innovations will have a great impact on user experience customization and interactivity, which will lower return rates and improve customer trust. This research also explains the application of modern technology such as deep learning and computer vision to increase the responsiveness and overall demand of virtual try-on capabilities.

Introduction

I. Overview

Virtual Try-On (VTO) technologies aim to enhance online shopping by allowing customers to virtually try on clothing items, addressing challenges such as dissatisfaction and high return rates associated with online apparel purchases. VTO systems can be categorized into:

• Image-Based Models: Provide quick garment previews using static images.goonline.io

• Multi-Pose Models: Offer dynamic try-ons by adapting to various poses.arxiv.org

• Video-Integrated Models: Deliver real-time, interactive try-on experiences through video integration.

Despite advancements, current VTO systems often rely on basic 2D overlays, which may not accurately represent garment fit and movement.

II. Data Collection

The VTO system was developed using apparel photos for simulation try-ons and user comparisons of API integration accuracy and participant interactivity during VTO testing. These data sources provided insights into existing VTO technologies and their limitations.

III. Literature Review

Research indicates that while advanced 3D virtual try-on systems offer higher accuracy, they require complex hardware and computational power, limiting scalability. AI-powered VTO features can significantly reduce customer hesitation and decrease returns. However, most contemporary VTO systems using pose recognition, texture mapping, and image stitching often fall short in dynamic, real-time customization. Innovative methods, including neural rendering and generative models like diffusion networks, are enhancing realism in try-on solutions. Augmented and virtual reality integrations also deliver immersive experiences that improve user confidence.

IV. Research Methodology

The research focused on integrating a VTO module into an e-commerce platform and assessing its performance and limitations.

• Website Development: Utilized React.js for the frontend, Node.js for the backend, and MongoDB for the database. Features included user registration, product browsing, and cart management.

• VTO Integration: Evaluated multiple third-party APIs, with RapidHub selected. However, the API offered only basic try-on functions through 2D superimposition, highlighting the limitations of current tools in replicating realistic garment fitting.

V. Challenges and Obstacles

• Technical Constraints: Realistic VTO simulations demand high computational power and advanced AI models, presenting a barrier for scalable implementation.

• Integration Issues: Many APIs lack proper documentation, complicating the development and integration process.

• User Experience Limitations: Garment misalignment, lack of body shape customization, and poor fabric simulation affect the realism and usability of VTO systems.

VI. Key Insights and Future Possibilities

• VTO Potential: Implementing Virtual Try-On features can improve customer satisfaction while decreasing user retention.

• Improvement Areas: Most user try-on APIs use prominent 2D images instead of a fully integrated 4D interactive simulation, disengaging the user from the experience.

• Emerging Solutions: Advancements in deep learning, augmented reality (AR), and real-time rendering are leading towards more natural and flexible VTO systems.

VII. System Architecture

The VTO system architecture comprises three layers:

1. Input Layer (Data): Users submit images and select clothing items.

2. Processing Layer: Utilizes the RapidHub API for image manipulation and try-on simulation.

3. Output Layer: Displays the processed image to the user.

MongoDB handles data storage operations in the background.

Conclusion

This study discusses integrating VTO functionalities into an existing e-commerce framework and reviews its existing technological features. Although RapidHub Integration APIs provide some level of service, there is still insufficiency of advanced, dynamic, and comprehensive solutions. Advanced research should focus on real-time clothing deformation, enhancing model-free generative AI rendering, and increasing the interactivity of AR applications. When mature, Virtual Try-On solutions have the potential to transform the digital retail space by enhancing accessibility, certainty, and interaction.

References

[1] T. Islam, A. Miron, X. Liu, and Y. Li, \"Deep Learning in Virtual Try-On: A Comprehensive Survey,\" IEEE Access, vol. 12, pp. 29475-29494, 2024. https://doi.org/10.1109/ACCESS.2024.3368612. [2] H. Sun, \"Virtual Try-On Methods: A Comprehensive Research and Analysis,\" Proc. 2023 Int. Conf. on Image, Algorithms and Artificial Intelligence (ICIAAI 2023), pp. 340-346, 2023. https://doi.org/10.2991/978-94-6463-300-9_35. [3] S. Zhou, J. Tang, Y. Xu, \"AI-Powered Fashion Fitting Models for E-commerce: A Comparative Study,\" Int. J. of Comp. Vision and Pattern Recognition, 2021. [4] M. Kim, L. Garcia, T. Huang, \"Real-Time Pose Transfer and Garment Warping for Virtual Try-Ons,\" ACM Trans. Graph., vol. 42, no. 4, 2023. [5] R. Banerjee, S. Mehta, \"Diffusion-Based Generative Models in Virtual Try-On Systems,\" IEEE CVPR Workshops, 2023. [6] K. Sharma, A. Patel, \"AR and VR Integration in Mobile-Based Try-On Applications,\" Int. J. of Augmented Reality Tech, 2023.

Copyright

Copyright © 2025 Alsafa Sayyed , Shatanaaz Gokak, Shakila Siddavatam. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.