解决服装图生成布料图案时细节失真的问题，提升生成质量与保真度。
Semi-supervised Latent Disentangled Diffusion Model for Textile Pattern Generation
- 通过解耦网络分离服装特征，构建独立的多维特征空间。
- 半监督扩散模型结合对齐策略，使生成图案在CTP-HD上FID降低4.1。
- 适用于高保真布料设计，尤其适合需要细节还原的工业场景。
纺织品图案生成（TPG）旨在基于给定服装图像合成精细的纺织品图案图像。尽管此前研究未专门探讨TPG，但现有图像到图像模型似乎是自然候选方案。然而，直接应用这些方法常产生不忠实的结果，因复杂纺织图案与服装图像中固有的非刚性纹理畸变之间存在特征混淆。本文提出一种新方法SLDDM-TPG，实现真实且高保真的纺织品图案生成。该方法分为两个阶段：(1) 潜在解耦网络（LDN），用于消除服装表征中的特征混淆，并构建多维独立的服装特征空间；(2) 半监督潜在扩散模型（S-LDM），接收来自LDN的引导信号，通过半监督扩散训练生成真实结果，并结合设计的细粒度对齐策略。大量评估表明，SLDDM-TPG在自建的CTP-HD数据集上将FID降低4.1，SSIM提升最高0.116；同时在VITON-HD数据集上也表现出良好泛化能力。
原文摘要 · Abstract (English)
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.
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