VirtualFit: AI virtual try-ons for fashion—enhancing confidence, cutting returns, and personalising shopping
Project Overview
VirtualFit is an AI-powered solution designed to transform the online shopping experience for clothing retailers. It allows users to virtually try on garments by overlaying clothing onto user-provided images, offering a realistic and interactive visualization of how items will fit and look before purchase.
Problem Statement
E-commerce clothing retailers faced high return rates caused by incorrect size selections and poor fit, resulting in dissatisfied customers and increased operational costs. The lack of a “try before you buy” option made online shopping less appealing, limiting personalization and reducing conversion rates.
Key Findings
- Purchase Confidence: Customers are significantly more likely to buy clothing when they can visualize how it will look on them, leading to higher conversion rates.
- Size and Fit Challenges: Size concerns and incorrect fit were identified as the leading causes of high return rates, impacting retailer profitability.
- Category Impact: Dresses, outerwear, and tailored items were among the categories most positively influenced by virtual try-ons, demonstrating higher engagement and lower return rates.
Implemented Solution
VirtualFit was developed to bridge the gap between physical and online shopping by providing an intuitive virtual try-on feature:
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AI-Powered Try-On Technology:
Developed a responsive solution that overlays apparel on user images with high visual fidelity, helping shoppers better evaluate product aesthetics and fit before purchasing.
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Size Mapping and Fit Accuracy:
Integrated size-matching algorithms using user body metrics to recommend the most suitable sizes, minimising size-related returns and enhancing fit precision.
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Real-Time Feedback Integration:
Enabled interactive adjustments for garment positioning, improving the realism and reliability of try-ons through dynamic user input and instant visual updates.
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Personalized Recommendations:
Utilized machine learning models to offer style and size suggestions tailored to each user’s preferences, past purchases, and body dimensions, fostering a more customised and satisfying shopping experience.
Results
VirtualFit delivered strong, measurable results for e-commerce retailers. Return rates were reduced by 25%, directly lowering costs and easing strain on logistics teams. Customer engagement and conversion rates increased by 30%, as the virtual try-on experience gave users greater confidence in their choices. The platform also enhanced customer satisfaction by offering a fun, interactive, and personalised shopping journey—helping retailers improve loyalty and stand out in a competitive market. VirtualFit has proven to be a game-changer for online apparel businesses aiming to merge technology with user-centric retail.