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.

基于扩散模型的条件修复技术在虚拟试衣中的有效使用，结合了扭曲模块的力量来产生高质量和逼真的虚拟试穿结果。

利用外观流驯服扩散模型的高质量虚拟试穿

Taming the Power of Diffusion Models for High-Quality Virtual Try-On  with Appearance Flow

Exemplar-based inpainting is the process of reconstructing missing parts of
an image by searching the remaining data for patches that fit seamlessly. The
image is completed to a plausible-looking solution by repeatedly inserting the
patch that is the best match according to some cost function. We present an
acceleration structure that uses a multi-index scheme to accelerate this search
procedure drastically, particularly in the case of very large datasets. The
index scheme uses ideas such as dimensionality reduction and k-nearest neighbor
search on space-filling curves that are well known in the field of multimedia
databases. Our method has a theoretic runtime of O(log2 n) per iteration and
reaches a speedup factor of up to 660 over the original method. The approach
has the advantage of being agnostic to most modelbased parts of exemplar-based
inpainting such as the order in which patches are processed and the cost
function used to determine patch similarity. Thus, the acceleration structure
can be used in conjunction with most exemplar-based inpainting algorithms.

使用多索引方案的加速结构来加速基于样本的修复，其具有 O（log2 n）的理论运行时间和高达 660 倍的加速因子。