Abstract
Virtual 3D fashion fitting, commonly referred to as 2D virtual try-on, has garnered significant attention due to its potential to revolutionize the way consumers interact with fashion items online. This paper presents a novel approach to virtual try-on utilizing a deep learning framework built upon the concept of appearance flow. Our proposed method improves the existing state-of-the-art techniques by seamlessly integrating natural cloth folds, shadows, and intricate textures, such as letters and comic characters, into the synthesized virtual try-on images. Building upon the advancements of previous research, our approach introduces a multi-faceted transformation strategy that operates at both the pixel and image patch levels. Our method’s effectiveness is demonstrated through extensive experiments and comparisons with existing virtual try-on techniques. The results showcase a substantial improvement in the synthesis of virtual try-on images with natural-looking cloth folds, realistic shadows, and intricate textures.