  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.
