Virtual Try-On System Using MediaPipe and OpenCV for AI-Based Clothing Overlay

Artificial Intelligence (AI) integration in fashion has created virtual try-on systems that improve the shopping experience of users in online shopping spaces. This paper discusses a real-time AI-based virtual try-on technique using MediaPipe and OpenCV to deliver an interactive and personalized clothing trial experience to the user. The system finds human pose keypoints like elbows, hips, and shoulders using MediaPipe to compute accurately the scale, position, and orientation for garment positioning. Processing webcam input, overlaying clothing images, and rendering the final display output uses OpenCV. In contrast to the conventional approaches that depend on pre-trained generative models or 3D avatar construction, this technique enables users dynamically to upload whatever clothing image they like, which in turn is remapped in real time onto their live webcam stream so that they can freely move while keeping garments aligned. The system provides a platform-independent, easy-to-use solution that accommodates flexible personalization and real-time responsiveness and plays a role in the continuous development of smart fashion technologies. The suggested framework is a lightweight replacement for deep generative models but achieves realistic performance and user experience in virtual garment trials.