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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<!-- Meta tags for social media banners, these should be filled in appropriatly as they are your "business card" -->
<!-- Replace the content tag with appropriate information -->
<meta name="description" content="Any2AnyTryon: Leveraging Adaptive Position Embeddings for Versatile Virtual Clothing Tasks">
<meta property="og:title" content="Any2AnyTryon"/>
<meta property="og:description" content="A versatile framework for virtual clothing tasks including garment reconstruction, model-free virtual try-on and virtual try-on"/>
<meta property="og:url" content="https://logn-2024.github.io/Any2anyTryon/"/>
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<!-- Keywords for your paper to be indexed by-->
<meta name="keywords" content="virtual try-on, garment reconstruction, diffusion models">
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<title>Any2AnyTryon</title>
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<h1 class="title is-1 publication-title">Any2AnyTryon: Leveraging Adaptive Position Embeddings for Versatile Virtual Clothing Tasks</h1>
<div class="is-size-5 publication-authors">
<!-- Paper authors -->
<span class="author-block">
<a href="https://github.com/logn-2024" target="_blank">Hailong Guo</a><sup>1</sup>,</span>
<span class="author-block">
<a href="#" target="_blank">Bohan Zeng</a><sup>2</sup>,</span>
<span class="author-block">
<a href="#" target="_blank">Yiren Song</a><sup>3</sup>
</span>
<span class="author-block">
<a href="#" target="_blank">Wentao Zhang</a><sup>2</sup>
</span>
<span class="author-block">
<a href="#" target="_blank">Chuang Zhang</a><sup>1</sup>
</span>
<span class="author-block">
<a href="#" target="_blank">Jiaming Liu</a><sup>4</sup>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><br><sup>1</sup>Beijing University of Posts and Telecommunications</span>
<span class="author-block"><br><sup>2</sup>Peking University</span><br>
<span class="author-block"><br><sup>3</sup>National University of Singapore</span>
<span class="author-block"><br><sup>4</sup>TiamatAI</span>
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<span>arXiv</span>
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<span>Demo</span>
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<span>Models</span>
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<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
Image-based virtual try-on (VTON) aims to generate a virtual try-on result by transferring an input garment onto a target person's image. However, the scarcity of paired garment-model data makes it challenging for existing methods to achieve high generalization and quality in VTON. Also, it limits the ability to generate mask-free try-ons. To tackle the data scarcity problem, approaches such as Stable Garment and MMTryon use a synthetic data strategy, effectively increasing the amount of paired data on the model side. However, existing methods are typically limited to performing specific try-on tasks and lack user-friendliness.
To enhance the generalization and controllability of VTON generation, we propose Any2AnyTryon, which can generate try-on results based on different textual instructions and model garment images to meet various needs, eliminating the reliance on masks, poses, or other conditions. Specifically, we first construct the virtual try-on dataset LAION-Garment, the largest known open-source garment try-on dataset. Then, we introduce adaptive position embedding, which enables the model to generate satisfactory outfitted model images or garment images based on input images of different sizes and categories, significantly enhancing the generalization and controllability of VTON generation. In our experiments, we demonstrate the effectiveness of our Any2AnyTryon and compare it with existing methods. The results show that Any2AnyTryon enables flexible, controllable, and high-quality image-based virtual try-on generation.
</p>
</div>
</div>
</div>
</div>
</section>
<!-- End paper abstract -->
<!-- Image carousel -->
<section class="hero is-small" style="display: none;">
<div class="hero-body">
<div class="container">
<h3 class="title is-4">Garment Reconstruction</h3>
<div id="results-garment" class="carousel results-carousel">
<div class="item">
<!-- Your image here -->
<img src="asset/images/supp_tryoff_wild.png" alt="Garment Reconstruction Results"/>
<h2 class="subtitle has-text-centered">
Garment reconstruction results in the wild.
</h2>
</div>
</div>
</div>
</div>
</section>
<!-- End image carousel -->
<!-- Image carousel -->
<section class="hero is-small">
<div class="hero-body">
<div class="container">
<h3 class="title is-4">Garment Reconstruction</h3>
<div id="results-garment" class="carousel results-carousel">
<div class="item">
<!-- Your image here style="max-height: 600px; width: auto;" -->
<img src="asset/images/supp_tryoff_wild.png" alt="Garment Reconstruction Results" />
<h2 class="subtitle has-text-centered">
Garment reconstruction results in the wild.
</h2>
</div>
</div>
</div>
</div>
</section>
<!-- End image carousel -->
<!-- Image carousel -->
<section class="hero is-small">
<div class="hero-body">
<div class="container">
<h3 class="title is-4">Model-free Virtual Try-on</h3>
<div id="results-model" class="carousel results-carousel">
<div class="item">
<!-- Your image here -->
<img src="asset/images/model_generation_supp.png" alt="Model-free VTON Results"/>
<h2 class="subtitle has-text-centered">
Model-free virtual tryon results
</h2>
</div>
</div>
</div>
</div>
</section>
<!-- End image carousel -->
<!-- Image carousel -->
<section class="hero is-small">
<div class="hero-body">
<div class="container">
<h3 class="title is-4">Virtual Try-on</h3>
<div id="results-vton" class="carousel results-carousel">
<div class="item">
<!-- Your image here -->
<img src="asset/images/tryon_compare.png" alt="VTON Results"/>
<h2 class="subtitle has-text-centered">
Virtual tryon results in the shop
</h2>
</div>
</div>
</div>
</div>
</section>
<!-- End image carousel -->
<!--BibTex citation -->
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>@misc{guo2025any2anytryonleveragingadaptiveposition,
title={Any2AnyTryon: Leveraging Adaptive Position Embeddings for Versatile Virtual Clothing Tasks},
author={Hailong Guo and Bohan Zeng and Yiren Song and Wentao Zhang and Chuang Zhang and Jiaming Liu},
year={2025},
eprint={2501.15891},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2501.15891},
}</code></pre>
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</section>
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