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

TL;DR

This paper introduces DCI-VTON, a diffusion-based method for virtual try-on that uses an exemplar-based warping module to preserve clothing details and produce high-quality, realistic images.

Contribution

It proposes a novel diffusion model approach with a warping module for detailed clothing preservation in virtual try-on tasks.

Findings

Outperforms existing GAN-based methods in quality and realism

Effectively preserves clothing details at high resolution

Demonstrates superiority on VITON-HD dataset

Abstract

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.