Style-VTON: A Novel Framework for Style-Guided Virtual Try-On

Abstract

Image-based virtual try-on technology aims to match in-store clothing with images of individuals wearing the garments accurately. However, current approaches often struggle to preserve the texture and pattern of the target clothing, particularly when dealing with complex poses. To address these limitations, we propose a novel framework, Style-VTON, for stylized image-based virtual try-on. This framework is specifically designed to maintain the fidelity of clothing textures and patterns, resulting in visually convincing try-on experiences. The Style-VTON framework comprises three specialized sub-networks, each performing a specific role. The first sub-network generates a highly detailed human parsing map, which accurately segments the person in the image. The second sub-network projects clothing patterns and textures onto the deformed body silhouette in real-time, ensuring spatial coherence and alignment. The third sub-network introduces user-driven customization, allowing for adjustments to garment texture, color, and style, thereby enhancing user experience through personalized virtual try-on results. Additionally, by leveraging the Pix22Dsurf method, the Style-VTON enhances its adaptability and achieves state-of-the-art performance across quantitative image quality benchmarks.