VTON-IT: Enhancing Virtual Try-Ons with YOLOv11 for Real-Time Fashion Visualization

This new design includes deep learning that eventually solves the major challenges of body occlusion, body transformations, and deformation of clothes in virtual simulation systems. It employs a multiple technology combination, such as CNN, YOLOv11 on image segmentation, edge detection, and bone feature extraction, so as to provide an extremely accurate and specific virtual experience for testing purposes. And, to make the clothing wear visible on a wide range of body types, this system relies on YOLOv11 to generate the intersection of garment and body. Further, advanced algorithms are used to tailor-fit the garment to the individual's body type so that the garment will remain beautiful and would not lose its focus on details. Reduce demand for the physical equipment that would improve the online experience to both customers and retailers. It enhances the skeleton-based feature extraction, thereby improving the fit of virtual clothing on many different types of bodies without sacrificing style. There is also detection of important body lines to further sharpen clothing boundaries and significantly increase the accuracy of virtual testing of digital clothing designs. This invention of blurring the digital-physical lines of marketing changes the landscape of the fashion business.