Try-On AI Fashion (FitSnap AI): A Computer Vision and 3D Reconstruction Framework for Virtual Outfit Fitting in E-Commerce

Shopping for clothes online remains a guessing game: a buyer can view a picture of an outfit but cannot see how it will actually look on their own body, so size and fit are decided on hope rather than evidence. This uncertainty is a leading cause of the high return rates that burden online fashion retailers. This paper presents Try-On AI Fashion (FitSnap AI), a web-based platform that lets a shopper virtually try on an outfit before purchase. The user uploads two photographs — one of themselves and one of the clothing item — and an AI pipeline combines them into a realistic try-on image within seconds. The system further validates the quality of the generated result, provides size advice derived from body measurements, builds a rotatable 360° 3D view of the fitted outfit, and generates a short walking preview video. The entire pipeline is built using free, open-source tools and runs on a standard laptop, requiring no paid software or dedicated cloud infrastructure. A review of recent virtual try-on literature shows that existing GAN-based, parser-free, and diffusion-based approaches stop at a flat two-dimensional output and do not offer an accessible application with 3D visualization, size guidance, and a low-cost setup — a gap this project addresses directly.