FashGAN: Conditional GAN-Based Virtual Clothing Design and Try-On System for Enhanced User Experience and Fashion Technology Innovation

Abstract

This project presents a Generative Artificial Intelligence–based fashion detection and recommendation system for automated outfit visualization. The system analyzes user-uploaded clothing images to identify apparel categories. Based on the identified category, a Conditional Generative Adversarial Network is employed to generate corresponding outfit images. The generated outputs provide an approximate visual representation rather than fully realistic results, highlighting the experimental nature of the system. A web-based application is developed to enable real-time image upload and result visualization. The system also offers basic fashion recommendations to assist users in styling decisions. Secure user authentication ensures controlled and personalized access. KEY WORDS: Generative Artificial Intelligence, Conditional Generative Adversarial Networks, Image-Based Apparel Classification, Fashion Image Synthesis, Virtual Outfit Generation, Fashion Recommendation System.