Virtual Try-On Solution for a Leading Fashion Brand

Background:

A leading fashion brand in India, sought to enhance customer experience and drive online sales by offering a virtual try-on feature for their clothing products. Leveraging Amazon Web Services (AWS), we developed a robust virtual try-on solution that accurately simulates how clothing items would look on customers. This innovative feature has significantly improved customer engagement, increased conversion rates, and enhanced brand loyalty.

Challenges Faced:

Client faced the challenge of providing a personalized shopping experience for customers, particularly for clothing items that require physical fitting. Traditional online shopping methods often led to product returns due to incorrect sizing or fit.

• Personalized Shopping Experience: Customers struggled to visualize how clothing would look and fit on their body types, leading to uncertainty and dissatisfaction.
• High Return Rates: Incorrect sizing and fit estimation often resulted in product returns, increasing operational costs.

Objectives

• Enhance Customer Engagement: Provide a more personalized and interactive online shopping experience.

• Boost Conversion Rates: Increase the likelihood of purchase decisions by allowing virtual trials of clothing.

• Reduce Product Returns: Improve fit accuracy and customer satisfaction to minimize returns

• Ensure Scalability: Build a cloud-native solution capable of handling high traffic and scaling as demand grows.

Solution Summary:

We adopted a cloud-native approach using AWS services to build a scalable and flexible Virtual Try-on solution. The solution involved:

• Image Processing: Utilized PoseNet for face detection, pose estimation, and landmark detection to accurately identify and map body positions.
• Generative AI Model: Built and trained a Stable Diffusion model using Amazon SageMaker Notebook to simulate how clothing items would fit different body types and poses.
• Deployment & Integration:Deployed the model using Amazon SageMaker Real-Time Inference Endpoint for real-time predictions and seamless performance. Stored images, models, and metadata securely in Amazon S3 for easy access and scalability.
• Web-Based Interface: Designed an intuitive, customer-friendly interface to allow users to interact with the virtual try-on feature. Hosted the web application using AWS Lambda and managed API endpoints via Amazon API Gateway for efficient communication.

Tech Stack:

• Development : Python

• Amazon SageMaker Notebook: For building and training the Gen AI model.

• Amazon SageMaker Real Time Inference Endpoint: For deploying the Gen AI model.

• Amazon S3: For storing images, models, and other data.

• AWS Lambda: For hosting the web application and API endpoints.

• Amazon API Gateway: API Management.