ML Powered Virtual Try-On: Transforming E-Commerce Fashion

The fashion sector confronts enormous difficulty with fit-related concerns, amounting to around $62 billion in yearly returns, with 70% of these returns ascribed to poor fit. There are intrinsic limits to getting a good fit when using traditional purchasing methods, both online and off. The goal of this research is to improve the accuracy, realism, and user experience of online garment fitting by developing a virtual try-on system based on machine learning. Through the use of cutting-edge computer vision methods like OpenCV and OpenPose for real-time body pose estimation, the system allows users to virtually try on clothes via an online interface. High-resolution camera video frames are captured as part of the process, which also includes body position detection, hand gesture recognition for clothing selection, and the overlaying of virtual clothes over the user's live video feed. Evaluation factors include technological performance, user experience, and impact on customer behaviour. According to preliminary findings, fit accuracy and user happiness have significantly improved, indicating that this technology may be able to close the gap between what customers expect from online purchasing and what they really experience. In the e-commerce fashion sector, the suggested system may lower return rates, improve consumer happiness, and increase engagement and sales.