A Style-Adapted Virtual Try-On Technique for Story Visualization

We propose a novel clothing application technique designed for story visualization framework where various characters appear wearing a wide range of outfits. To achieve our goal, we extend a Virtual Try-On framework for synthetic garment fitting. Conventional Virtual Try-On methods are limited to generating images of a single person wearing a restricted set of clothes within a fixed style domain. To overcome these limitations, we apply an improved Virtual Try-On model trained with appropriately processed datasets, enabling the generation of upper and lower garments separately across diverse characters and producing images in four distinct styles: photorealistic, webtoon, animation, and watercolor. Our system collects character images and clothing images and performs accurate masking of garment regions. Our system takes a style-specific text prompt as input. Based on these inputs, garment-specific conditioning is applied to synthesize the clothing, followed by a cross-style diffusion process that generates Virtual Try-On images reflecting multiple visual styles. Our approach significantly enhances the adaptability and stylistic diversity of Virtual Try-On technology for story visualization applications.