CG-VTON: Controllable Generation of Virtual Try-On Images Based on Multimodal Conditions

Transforming fashion design sketches into realistic garments remains a challenging task due to the reliance on labor-intensive manual workflows that limit efficiency and scalability in traditional fashion pipelines. While recent advances in image generation and virtual try-on technologies have introduced partial automation, existing methods still lack controllability and struggle to maintain semantic consistency in garment pose and structure, restricting their applicability in real-world design scenarios. In this work, we present CG-VTON, a controllable virtual try-on framework designed to generate high-quality try-on images directly from clothing design sketches. The model integrates multi-modal conditional inputs, including dense human pose maps and textual garment descriptions, to guide the generation process. A novel pose constraint module is introduced to enhance garment-body alignment, while a structured diffusion-based pipeline performs progressive generation through latent denoising and global-context refinement. Extensive experiments conducted on benchmark datasets demonstrate that CG-VTON significantly outperforms existing state-of-the-art methods in terms of visual quality, pose consistency, and computational efficiency. By enabling high-fidelity and controllable try-on results from abstract sketches, CG-VTON offers a practical and robust solution for bridging the gap between conceptual design and realistic garment visualization.