Semi-supervised Latent Disentangled Diffusion Model for Textile Pattern Generation
This addresses the challenge of generating fine-grained textile patterns for fashion and design applications, representing an incremental improvement over existing image-to-image models.
The paper tackles the problem of textile pattern generation from clothing images by proposing SLDDM-TPG, a method that reduces feature confusion and improves fidelity, achieving a 4.1 reduction in FID and up to 0.116 improvement in SSIM on the CTP-HD dataset.
Textile pattern generation (TPG) aims to synthesize fine-grained textile pattern images based on given clothing images. Although previous studies have not explicitly investigated TPG, existing image-to-image models appear to be natural candidates for this task. However, when applied directly, these methods often produce unfaithful results, failing to preserve fine-grained details due to feature confusion between complex textile patterns and the inherent non-rigid texture distortions in clothing images. In this paper, we propose a novel method, SLDDM-TPG, for faithful and high-fidelity TPG. Our method consists of two stages: (1) a latent disentangled network (LDN) that resolves feature confusion in clothing representations and constructs a multi-dimensional, independent clothing feature space; and (2) a semi-supervised latent diffusion model (S-LDM), which receives guidance signals from LDN and generates faithful results through semi-supervised diffusion training, combined with our designed fine-grained alignment strategy. Extensive evaluations show that SLDDM-TPG reduces FID by 4.1 and improves SSIM by up to 0.116 on our CTP-HD dataset, and also demonstrate good generalization on the VITON-HD dataset.