Reimagining Retail Try-On Experiences Using an AI-Enabled Virtual Fitting Mirror

The rapid evolution of artificial intelligence and computer vision technologies has created new opportunities to enhance customer experiences in physical retail environments. Traditional clothing try-on processes rely on repeated physical fitting, which is time-consuming, inefficient, and often leads to customer dissatisfaction and high product return rates. This paper proposes an AI-enabled virtual fitting mirror designed to reimagine retail try-on experiences through realtime body measurement extraction and intelligent apparel simulation. The proposed framework integrates computer vision, machine learning, and garment data modeling to capture a customer’s body dimensions via a guided 360-degree scan and generate a realistic visual simulation of selected clothing items without requiring physical trial. The system assumes the existence of a centralized garment database containing precise dimensional and material attributes, enabling accurate size matching and visual rendering. By producing short video-based simulations of garments worn on the customer’s digital body model, the framework reduces fitting room dependency while improving decision accuracy and shopping efficiency. The proposed approach offers a scalable, data-driven solution for smart retail environments, with the potential to reduce return rates, optimize inventory utilization, and enhance customer satisfaction. This work contributes a conceptual and architectural foundation for AI-driven virtual try-on systems, positioning intelligent fitting mirrors as a practical and transformative component of future retail ecosystems.