Despite their impressive generative performance, latent diffusion model-based
virtual try-on (VTON) methods lack faithfulness to crucial details of the
clothes, such as style, pattern, and text. To alleviate these issues caused by
the diffusion stochastic nature and latent supervision, we propose a novel
Faithful Latent Diffusion Model for VTON, termed FLDM-VTON. FLDM-VTON improves
the conventional latent diffusion process in three major aspects. First, we
propose incorporating warped clothes as both the starting point and local
condition, supplying the model with faithful clothes priors. Second, we
introduce a novel clothes flattening network to constrain generated try-on
images, providing clothes-consistent faithful supervision. Third, we devise a
clothes-posterior sampling for faithful inference, further enhancing the model
performance over conventional clothes-agnostic Gaussian sampling. Extensive
experimental results on the benchmark VITON-HD and Dress Code datasets
demonstrate that our FLDM-VTON outperforms state-of-the-art baselines and is
able to generate photo-realistic try-on images with faithful clothing details.

虽然虚拟试衣（VTON）基于潜在扩散模型的生成性能令人印象深刻，但缺乏对衣物关键细节（如风格、图案和文字）的忠实性。为了缓解由扩散随机性和潜在监督引起的这些问题，我们提出了一种新颖的面向 VTON 的忠实潜在扩散模型，称为 FLDM-VTON。FLDM-VTON 在三个主要方面改进了传统的潜在扩散过程。首先，我们提议将变形的衣物作为起点和局部条件，为模型提供忠实的衣物先验。其次，我们引入了一种新颖的衣物扁平化网络，以约束生成的试衣图像，提供一致的衣物忠实监督。第三，我们设计了一种具有忠实推理的衣物后验采样，进一步提高了模型在传统的衣物不可知高斯采样上的性能。对基准 VITON-HD 和 Dress Code 数据集的大量实验证明，我们的 FLDM-VTON 优于最先进的基线方法，并能够生成具有忠实衣物细节的照片级别的试衣图像。