AI-Powered Virtual Try-On System: Enhancing Fit Prediction and User Comfort Through Deep Learning

The rapid growth of e-commerce in the fashion industry has fueled the demand for advanced virtual try-on solutions to enhance the client experience. However, existing systems often need to deliver precise fit prediction, comprehensive comfort analysis, and interactive precision, contributing to high return rates and low user satisfaction. This paper presents an AI-powered virtual try-on system to address these challenges by leveraging state-of-the-art deep learning and computer vision techniques. The proposed system integrates gesture-based controls for seamless interaction, real-time body tracking for enhanced accuracy, and a personalized fashion recommender utilizing prior purchase data to improve user engagement. Key innovations include using AI-generated heat maps for garment comfort assessment, achieving a fit prediction accuracy of 94.1%, and demonstrating a 20% improvement in user satisfaction compared to existing systems, as measured by survey feedback. The system also reduces computational complexity while ensuring scalability and practical applicability in real-world e-commerce scenarios. By combining intuitive design, personalization, and advanced analytics, this research establishes a robust framework for next-generation virtual try-on technologies to revolutionize the online shopping experience.