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 real
time 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.