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<!DOCTYPE html>
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<head>
<meta charset="utf-8">
<meta name="description"
content="AnyDressing: Customizable Multi-Garment Virtual Dressing via Latent Diffusion Models. ">
<meta name="keywords" content="customize characters wearing any combination of garments!">
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<title>AnyDressing: Customizable Multi-Garment Virtual Dressing via Latent Diffusion Models</title>
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<h1 class="title is-1 publication-title"><span style="
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AnyDressing
</span>: Customizable Multi-Garment Virtual Dressing via Latent Diffusion Models</h1>
<div class="is-size-5 publication-authors">
<!-- Paper authors -->
<span class="author-block">
<a href="https://github.com/Crayon-Shinchan">Xinghui Li</a><sup>1</sup>,</span>
<span class="author-block">
<a href="https://github.com/sun631998316">Qichao Sun</a><sup>1</sup>,</span>
<span class="author-block">
<a href="https://github.com/PangzeCheung">Pengze Zhang</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=-BbQ5VgAAAAJ&hl=en&oi=ao">Fulong Ye</a><sup>1</sup>,
</span>
<span class="author-block">
<a
href="https://scholar.google.com.hk/citations?user=4eRwbOEAAAAJ&hl=zh-CN">Zhichao Liao</a><sup>2</sup>
</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?hl=zh-CN&user=na2akZYAAAAJ&view_op=list_works&sortby=pubdate">Wanquan Feng</a><sup>1✉</sup>,</span>
</span>
<br>
<span class="author-block">
<a href="https://crayon-shinchan.github.io/AnyDressing/">Songtao Zhao</a><sup>1✉</sup>,
</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?view_op=list_works&hl=zh-CN&authuser=1&user=9rWWCgUAAAAJ">Qian He</a><sup>1</sup>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>ByteDance,</span>
<span class="author-block"><sup>2</sup>Tsinghua University</span> 
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>✉</sup>Corresponding author</span> 
</div>
<!-- <div class="is-size-5 publication-authors">
<p style="color: green;">We'd like to thank Yongsheng Dong and Guoliang Xu, for the valuable help on data processing.</p>
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<span>Paper</span>
</a>
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<a href="https://github.com/Crayon-Shinchan/AnyDressing" class="external-link button is-normal is-rounded is-dark">
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<span>Code</span>
</a>
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</section>
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<p>
<b><i>Reliability: </i></b> AnyDressing is well-suited for a variety of scenes and complex garments.
</p>
<p>
<b><i>Compatibility: </i></b> AnyDressing is compatible with LoRA and plugins such as ControlNet and FaceID.
</p>
</h2>
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<p>
Recent advances in garment-centric image generation from text and image prompts based on diffusion models are impressive. However, existing methods lack support for various combinations of attire, and struggle to preserve the garment details while maintaining faithfulness to the text prompts, limiting their performance across diverse scenarios.
In this paper, we focus on a new task, i.e., <b><i>Multi-Garment Virtual Dressing</i></b>, and we propose a novel <b>AnyDressing</b> method for customizing characters conditioned on any combination of garments and any personalized text prompts.
AnyDressing comprises two primary networks named GarmentsNet and DressingNet, which are respectively dedicated to extracting detailed clothing features and generating customized images.
Specifically, we propose an efficient and scalable module called Garment-Specific Feature Extractor in GarmentsNet to individually encode garment textures in parallel. This design prevents garment confusion while ensuring network efficiency.
Meanwhile, we design an adaptive Dressing-Attention mechanism and a novel Instance-Level Garment Localization Learning strategy in DressingNet to accurately inject multi-garment features into their corresponding regions. This approach efficiently integrates multi-garment texture cues into generated images and further enhances text-image consistency.
Additionally, we introduce a Garment-Enhanced Texture Learning strategy to improve the fine-grained texture details of garments.
Thanks to our well-craft design, AnyDressing can serve as a plug-in module to easily integrate with any community control extensions for diffusion models, improving the diversity and controllability of synthesized images.
Extensive experiments show that AnyDressing achieves state-of-the-art results.
</p>
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Given <i>N</i> target garments, AnyDressing customizes a character dressed in multiple target garments.
The GarmentsNet leverages the <b>Garment-Specific Feature Extractor (GFE)</b> module to extract detailed features from multiple garments.
The DressingNet integrates these features for virtual dressing using a <b>Dressing-Attention (DA)</b> module and an <b>Instance-Level Garment Localization Learning</b> mechanism.
Moreover, <b>Garment-Enhanced Texture Learning (GTL)</b> strategy further enhances details.
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<img src="static/images/compare_1.png" alt="" width="2160" />
<p>
<b>Qualitative comparisons</b> with state-of-the-art methods of single-garment and multi-garment dressing.
</p>
</td>
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<h2 class="title is-2 has-text-centered" style="font-family: Comic Sans MS"><div class="x-gradient-font">Various Virtual Dressing Results</div></h2>
<h2 class="title is-4 has-text-centered"><b style="color:#008AD7; font-weight:normal">▶ </b> Realistic Scenes</h2>
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<img src="static/images/hat2.png" alt="" style="width: 92%;" />
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<h2 class="title is-4 has-text-centered"><b style="color:#008AD7; font-weight:normal">▶ </b> <b><i>AnyDressing</i></b> + IPA & ControlNet</h2>
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<h2 class="title is-4 has-text-centered"><b style="color:#008AD7; font-weight:normal">▶ </b> <b><i>AnyDressing</i></b> + LoRA</h2>
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<h2 class="title is-4 has-text-centered"><b style="color:#008AD7; font-weight:normal">▶ </b> DreamBooth/LoRA + <b><i>DEADiff</i></b></h2>
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<h2 class="title is-4 has-text-centered"><b style="color:#008AD7; font-weight:normal">▶ </b> Stylized Reference Object Generation</h2>
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This is a novel application of <b><i>DEADiff</i></b>. Instructed by the "content" condition, <b><i>DEADiff</i></b> can extract the semantics of a reference image and achieve its stylization under the guidance of an extra text prompt.
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<p>
If you find this project useful in your research, please consider cite:
</p>
<pre><code>@article{li2024anydressing,
title={AnyDressing: Customizable Multi-Garment Virtual Dressing via Latent Diffusion Models},
author={Xinghui Li and Qi Chao Sun and Pengze Zhang and Fulong Ye and Zhichao Liao and Wanquan Feng and Songtao Zhao and Qian He},
journal={arXiv preprint arXiv:2412.04146},
year={2024}
}</code></pre>
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</section>
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<p>
This project is intended solely for academic research and effect demonstration. No commercial benefits are derived from it. Most models and clothing images used are from internet and public dataset (VITON-HD).
All images and brands are the property of their respective owners.
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Website source code based on the <a
href="https://github.com/nerfies/nerfies.github.io">Nerfies</a> project page.
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