AI-Powered Virtual Try-On System: VITON-HD with Intelligent Recommendation Engine

Abstract

This work introduces an advanced virtual try-on system that combines high-resolution garment transfer with AI-driven technical approach-driven recommendation features. Built upon the VITON-HD (Virtual Try-On High-Definition) systematic structure design, the developed platform integrates neural network-based learning-based image synthesis with intelligent clothing recommendation algorithms to provide a comprehensive solution for online fashion retail. The platform features three core components: a Segmentation Generator for body parsing, a Geometric Matching Module (GMM) for precise clothing warping using Thin-Plate Spline (TPS) transformation, and an ALIAS Generator for photo-realistic image synthesis. Beyond the core try-on functionality, We establish novel features including AI-powered clothing recommendations based on color harmony analysis, skin tone classification for personalized shopping experiences, real-time AR try-on using webcam input, and an intuitive web interface for seamless user interaction. Evaluation outcomes demonstrate that the developed platform achieves high-quality virtual try on results at 768×1024 resolution while maintaining computational computational effectiveness suitable for consumer hardware. The integrated AI recommendation system shows substantial enhancement in user engagement and conversion rates. This platform addresses critical challenges in e-commerce by enabling customers to visualize clothing fit and appearance before purchase, reducing return rates and improving customer satisfaction.