Real-Time Virtual Try-On Using AI and Augmented Reality for Personalized Fashion Experience

The rise of digital fashion commerce has increased the demand for interactive and personalized shopping experi- ences. Virtual Try-On (VTO) technology addresses this need by allowing users to visualize garments on their own images, reducing uncertainty around fit and appearance. This paper presents a deep learning-based VTO system that uses a 2D-to-2D transformation pipeline, leveraging U²-Net for human segmenta- tion and a Segmind-powered diffusion model for realistic garment synthesis. The system includes a lightweight Flask backend, a chatbot interface for collecting user traits such as gender, skin tone, and body type, and a recommendation engine for contextual outfit suggestions. A trend visualization module further supports decision-making by displaying gender-specific clothing trends. Despite being trained on a limited dataset ( 2.5 GB), the system shows improved overlay accuracy and personalization compared to earlier models like VITON. Designed with modularity and scalability in mind, this implementation demonstrates the poten- tial of AI-driven virtual try-on in enhancing online fashion retail.