Abstract:
Virtual Try-On (VTO) systems have revolutionized the e-commerce landscape by enabling users to visualize how clothing items would look on their images, bridging the gap between online and in-store shopping experiences. These systems enhance user engagement, improve decision-making, and significantly reduce product return rates. However, existing VTO technologies face critical challenges. 3D modeling approaches, while highly accurate, require substantial computational resources and detailed 3D data, which is often unavailable. On the other hand, traditional 2D image-based methods struggle with aligning clothing accurately to the user’s body shape and pose, often leading to unrealistic results. This highlights a pressing need for solutions that balance accuracy, computational efficiency, and scalability. To address these limitations, VirtualFit is introduced as an advanced image-based Virtual Try-On framework designed to deliver a seamless, intuitive, and highly realistic virtual try-on experience for e-commerce applications. The framework adopts a multi-model architecture that leverages state-of-the-art deep learning and computer vision technologies. It integrates HR-VITON for high-resolution virtual fitting, PoseNet for precise body pose estimation, Graphonomy for detailed semantic segmentation, Detectron2 for robust object detection, and a dedicated cloth image segmentation module to capture intricate details of clothing items. Together, these models enable accurate, high-quality overlays of selected clothing onto user-uploaded images without relying on computationally expensive 3D data. The effectiveness of VirtualFit is validated through extensive experiments conducted on a diverse dataset sourced from online retail platforms, encompassing various body types, clothing styles, and poses. Results demonstrate significant improvements over existing image-based virtual try-on methods in terms of accuracy, realism, and computational efficiency. User studies further emphasize the system’s ability to provide a more engaging and satisfying online shopping experience, with participants highlighting the high level of detail and accuracy in the generated visualizations. VirtualFit represents a significant leap forward in the field of Virtual Try-On technologies, addressing the limitations of current methods while offering a scalable and practical solution for e-commerce platforms. Future directions for this technology include incorporating dynamic try-on capabilities, where users can visualize clothing during movement, and extending compatibility to accessories and multi-layered outfits. Additionally, advancements in AI-driven fabric simulation and texture modeling will further enhance realism, offering deeper personalization by integrating user-specific preferences and stylistic suggestions. These developments aim to redefine online shopping further, enhance customer confidence, and contribute to more sustainable and user-friendly retail experiences.
