Virtual try-on is a critical image synthesis task that aims to transfer clothes from one image to another while preserving the details of both humans and clothes. While many existing methods rely on Generative Adversarial Networks (GANs) to achieve this, flaws can still occur, particularly at high resolutions. Recently, the diffusion model has emerged as a promising alternative for generating high-quality images in various applications. However, simply using clothes as a condition for guiding the diffusion model to inpaint is insufficient to maintain the details of the clothes. To overcome this challenge, we propose an exemplar-based inpainting approach that leverages a warping module to guide the diffusion model's generation effectively. The warping module performs initial processing on the clothes, which helps to preserve the local details of the clothes. We then combine the warped clothes with clothes-agnostic person image and add noise as the input of diffusion model. Additionally, the warped clothes is used as local conditions for each denoising process to ensure that the resulting output retains as much detail as possible. Our approach, namely Diffusion-based Conditional Inpainting for Virtual Try-ON (DCI-VTON), effectively utilizes the power of the diffusion model, and the incorporation of the warping module helps to produce high-quality and realistic virtual try-on results. Experimental results on VITON-HD demonstrate the effectiveness and superiority of our method.

翻译：虚拟试穿是一项关键的图像合成任务，旨在将服装从一张图像转移到另一张图像，同时保留人体和服装的细节。尽管许多现有方法依赖生成对抗网络（GANs）来实现此目标，但仍可能出现瑕疵，尤其是在高分辨率情况下。近年来，扩散模型作为生成高质量图像的有前景替代方案，已在多种应用中崭露头角。然而，仅将服装作为条件来引导扩散模型进行修复，不足以保留服装的细节。为克服这一挑战，我们提出了一种基于示例的修复方法，利用扭曲模块有效引导扩散模型的生成过程。该扭曲模块对服装进行初步处理，有助于保留服装的局部细节。随后，我们将扭曲后的服装与服装无关的人体图像结合，并添加噪声作为扩散模型的输入。此外，扭曲后的服装被用作每个去噪过程的局部条件，以确保输出结果尽可能多地保留细节。我们的方法，即基于扩散的条件修复用于虚拟试穿（DCI-VTON），有效利用了扩散模型的能力，而扭曲模块的融入则有助于生成高质量且逼真的虚拟试穿结果。在VITON-HD数据集上的实验结果表明了我们方法的有效性和优越性。