AI Virtual Try-Ons for Retail

Increase shopper confidence and reduce return rates with AI-powered virtual try-ons.
With AWS, retailers can deliver immersive, personalized try-on experiences directly within their digital storefronts—boosting conversions and customer satisfaction.

The Challenge

In online fashion, beauty, and accessories retail, one of the top reasons for customer hesitation and high return rates is the inability to try products before buying. Customers want to see how clothes fit, how glasses look on their face, or how a lipstick shade complements their skin tone—all without visiting a store. Traditional product photos and size guides fall short in delivering personalized confidence.

Solution Overview

AWS enables retailers to implement virtual try-on experiences using a combination of computer vision, machine learning, and Generative AI services like Amazon Bedrock, FMs, Amazon Rekognition. These services allow shoppers to visualize how products look on their face, body, or in their environment, through web or mobile apps—leading to more informed purchases.

Key Capabilities

• Facial & body landmark detection: Precisely map faces or body shapes for accurate try-on alignment
• Real-time visualization: Let users virtually “wear” clothing, glasses, makeup, or accessories
• Omnichannel deployment: Integrate into mobile apps, kiosks, or websites
• Personalized recommendations: Suggest sizes, styles, or colors based on shopper profile and visual fit
• Scalable backend: Run computer vision pipelines at scale with high performance and low latency

Business Value

• Increase conversion rates by giving shoppers more confidence to purchase
• Reduce return rates with better product fit and visual accuracy
• Enhance customer experience with interactive, personalized features
• Differentiate your brand with immersive and modern shopping journeys
• Lower operational costs by minimizing size-related returns and restocking

Use Cases

• Virtual eyewear fitting using face mapping and 3D rendering
• Cosmetics try-on (lipstick, blush, foundation) using live camera feeds
• Clothing try-on for t-shirts, dresses, or outerwear with body landmarking
• Footwear try-on using phone camera and foot detection
• Accessory try-on (e.g., earrings, hats, watches) in mobile or web
• Style suggestions based on what users try or like
• AI-assisted size guidance based on visual proportions and past purchases

Customer Readiness Checklist for POC

• List of product types for try-on (e.g., glasses, shirts, lipstick)
• Product image assets and 3D models (if available)
• Brand styling guidelines for rendering
• Success Criteria: Clearly state the basic technical outcome needed for the POC to be successful

Architecture, cost estimation, POC timeline

POC timeline