APPLYING POSE-GUIDED DEEP LEARNING FOR REAL-TIME VIRTUAL TRY-ON

This project presents a real-time virtual try-on system that applies pose-guided deep learning techniques to accurately fit and visualize clothing on a user’s 3D body model. Leveraging OpenCV for webcam capture, MediaPipe for real-time pose estimation, TensorFlow for size prediction, and Unity3D for interactive 3D visualization, the system enables users to virtually try on clothes with realistic fitting and 360° viewing capabilities. This seamless integration of cutting-edge computer vision and deep learning technologies enhances online shopping experiences by delivering personalized and engaging virtual try-ons.