Interactive Virtual Fitting Room: Real-Time E Commerce Accessories Try-on with Image Descriptors using Deep Learning

The drastic growth of e-business has created a need for innovative ways to enhance the online shopping experience, particularly for accessories such as eyewear, jewellery, and T-shirts. The paper focuses on revolutionizing the online shopping experience by introducing a virtual try-on system for customers. This innovative approach employs deep learning, specifically Convolutional Neural Networks (CNNs), to create a sophisticated virtual wardrobe. The CNN model is trained to detect complex details of clothing items, including attributes such as fit, style, and color. The foundation of this system lies in a prepared dataset that encompasses a diverse range of high-quality images, each labeled with relevant information. Through the training process, the CNN model refines its understanding, adapting to the nuances of different garments. To enhance realism, the system incorporates cloth deformation, simulating how fabrics drape and fold based on the user's body shape and movements. Real-time rendering is a pivotal aspect, ensuring a seamless and immediate representation of the selected product on the user's real time data. The synergy of CNN-based attribute recognition, cloth deformation, and real-time rendering culminates in an immersive and lifelike virtual try-on experience. Users gain the ability to visualize how a chosen products might look on them, empowering them with a more informed perspective for confident and satisfying online purchases.