UST Virtual Try-On: AI-Powered Virtual Fitting Room for Retail

Overview

UST Virtual Try-On is an AI- and AR-powered virtual try-on platform that brings real-time visualization to fashion, beauty, eyewear, footwear, and accessories. Shoppers can instantly see products on themselves across web, mobile apps, and in-store smart mirrors, creating an immersive virtual fitting room and virtual dressing room experience. Built as a cloud-native solution on AWS, UST Virtual Try-On combines computer vision, 3D modelling, and body landmark detection to deliver lifelike fit, drape, texture, and color accuracy.

The platform provides a unified, omnichannel shopping journey for fashion and retail brands, with prebuilt SDKs that integrate into existing ecommerce sites, mobile applications, and in-store digital experiences. A centralized dashboard helps teams manage 3D assets and SKUs, monitor try-on behavior, and analyze performance across channels. Retailers use UST Virtual Try-On to increase ecommerce conversion, reduce product returns, and strengthen shopper confidence—while leveraging AWS for performance, scalability, and global reach in AI AR retail and omnichannel shopping.

Features

• AI-based body and facial landmark detection: Accurately maps facial and full-body points to align apparel, footwear, eyewear, and beauty products to each shopper’s unique shape, pose, and movement.

• High-fidelity 3D rendering with true-to-color accuracy: Uses advanced 3D visualization and rendering pipelines to replicate fabric drape, texture, gloss, and shading so products look realistic under different lighting and angles.

• Cross-channel SDKs for ecommerce, app, and smart mirror integration: Lightweight SDKs and APIs allow rapid integration with retail websites, mobile apps, and in-store smart mirrors, enabling a consistent virtual try-on experience across all touchpoints.

• Real-time AI + AR visualization: Combines augmented reality overlays with real-time computer vision tracking so shoppers can move naturally while seeing products rendered on their live image or video feed.

• Social share and compare capabilities: Built-in social share and side-by-side comparison features let shoppers capture looks, share with friends, and compare outfits, increasing engagement and time on site.

• 3D asset and SKU management with analytics: Centralized tools to onboard, manage, and update 3D assets and product SKUs, with analytics on try-on frequency, product engagement, and conversion performance across categories.

• Analytics on try-on frequency, conversion, and fit insights: Dashboards and reports surface key metrics such as try-on events, add-to-cart rates, conversion uplift, and items frequently tried but not purchased, informing merchandising and fit strategies.

• Secure APIs for catalog, inventory, and order system connections: Enterprise-grade APIs integrate with ecommerce catalogs, inventory systems, and order management, ensuring product data accuracy and secure data flows in AWS retail environments.

• Cloud-ready architecture on AWS: Designed as a cloud-native platform leveraging AWS for elasticity, performance, reliability, and security, supporting pilots through to large-scale global deployments.

Benefits

• Increase conversion rates and revenue: Drive measurable uplift in digital sales—retailers typically see 25–40% conversion uplift when shoppers can virtually try on products before purchasing.

• Reduce product returns and reverse logistics costs: More accurate visualization of fit, drape, and style leads to 20–30% reduction in returns, lowering shipping, restocking, and customer support costs.

• Boost shopper confidence and satisfaction: Shoppers gain confidence by seeing items on themselves instead of static product images, improving satisfaction, reviews, and long-term loyalty.

• Deliver a seamless omnichannel experience: Offer the same virtual try-on experience across web, mobile, and store-based smart mirrors, supporting consistent brand storytelling and unified customer journeys.

• Accelerate time to value with a 14–18 week rollout: A structured pilot-to-deployment program, from discovery and readiness assessment through SDK integration, enables retailers to go live quickly and scale with lower implementation risk.

• Optimize merchandising and product strategy: Use try-on and conversion analytics to identify high-performing SKUs, uncover fit issues, refine assortments, and inform design and buying decisions.

• Differentiate your brand with immersive AI AR retail: Stand out in crowded ecommerce and D2C markets by delivering a premium virtual try-on and virtual fitting room experience that aligns with modern shopper expectations.

• Scale globally with AWS-powered performance and security: Rely on AWS infrastructure to meet performance, compliance, and security needs as traffic grows, seasonal peaks hit, and new regions or markets are added.

Highlights

• AI- and AR-powered virtual try-on platform that delivers real-time visualization of fashion, beauty, footwear, and accessories, letting shoppers see products on themselves across web, mobile apps, and in-store smart mirrors for immersive omnichannel shopping.

• Intelligent virtual try-on recommendations and analytics with a 3D asset/SKU management dashboard that tracks try-on behavior, conversion, and returns—helping retail and D2C brands tune assortments, resolve fit issues, and improve overall AI AR retail performance.

• Cloud-native Virtual Try-On on AWS with a 14–18 week pilot-to-deployment timeline, prebuilt SDKs, and secure APIs to enable launch and scale AI AR retail experiences quickly while meeting performance, security, and scalability requirements.