VIRTUAL FITTING ROOM

Abstract

Virtual try-on (VTO) technology is a rapidly advancing domain that utilizes computer vision and machine learning to transform how consumers engage with fashion and ecommerce. By enabling users to visualize garments or accessories on their own bodies virtually, VTO addresses critical challenges in online shopping, such as inaccurate size selection, high return rates, and customer dissatisfaction. This thesis focuses on applying Flux, an advanced generative model, to enhance the quality and accuracy of virtual try-on systems. Compared to popular frameworks like Stable Diffusion, Flux demonstrates superior performance in preserving garment details, maintaining body realism, and generating visually convincing results. The study benchmarks Flux against Stable Diffusion-based methods, highlighting its advantages in terms of fidelity, adaptability to diverse body types, and scalability for commercial applications. Moreover, this research emphasizes the practicality of implementing Flux-powered VTO systems in e-commerce platforms, particularly for online fashion retailers. By offering highly personalized, interactive, and realistic try-on experiences, Flux can drive higher customer satisfaction and reduce return rates, contributing to operational efficiency and profitability. This study aims to bridge the gap between advanced generative models and real-world commercial applications, enabling a new era of virtual shopping experiences.