Semi-supervised Latent Disentangled Diffusion Model for Textile Pattern Generation

TL;DR

This paper introduces SLDDM-TPG, a novel semi-supervised diffusion model with a latent disentangled network for faithful, high-fidelity textile pattern generation from clothing images, addressing feature confusion issues.

Contribution

It proposes a two-stage approach combining a latent disentangled network and a semi-supervised diffusion model for improved textile pattern synthesis.

Findings

Reduces FID by 4.1 on CTP-HD dataset

Improves SSIM by up to 0.116

Demonstrates good generalization on VITON-HD dataset

Abstract

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.