半监督潜在解耦扩散模型用于纺织图案生成
Semi-supervised Latent Disentangled Diffusion Model for Textile Pattern Generation
AI总结：
本文提出SLDDM-TPG方法，通过潜在解耦网络和半监督扩散模型生成高保真的纺织图案，有效解决服装与纹理混淆问题。
摘要：
纺织图案生成（TPG）旨在基于给定的服装图像合成精细的纺织图案图像。尽管先前研究未明确调查TPG，现有图像到图像模型似乎是该任务的自然候选。然而，直接应用这些方法往往会产生不忠实的结果，由于复杂纺织图案与服装图像固有非刚性纹理变形之间的特征混淆而无法保留细粒度细节。本文提出了一种新的方法SLDDM-TPG，用于忠实且高保真的TPG。我们的方法分为两个阶段：（1）一个潜在解耦网络（LDN），解决服装表示中的特征混淆，并构建多维、独立的服装特征空间；（2）一个半监督潜在扩散模型（S-LDM），通过接收来自LDN的指导信号，并通过半监督扩散训练生成忠实结果，结合我们设计的细粒度对齐策略。广泛评估显示，SLDDM-TPG在我们的CTP-HD数据集上将FID降低4.1，并将SSIM提高至0.116，同时在VITON-HD数据集上也表现出良好的泛化能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
