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<!DOCTYPE html> | ||
<html lang="en"> | ||
<head> | ||
<meta charset="UTF-8"> | ||
<meta http-equiv="x-ua-compatible" content="ie=edge"> | ||
<title>DLT</title> | ||
<meta name="description" content=""> | ||
<meta name="viewport" content="width=device-width, initial-scale=1"> | ||
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<!-- Bootstrap CSS --> | ||
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.5/css/bootstrap.min.css"> | ||
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<!-- Font Awesome CSS --> | ||
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/font-awesome/4.4.0/css/font-awesome.min.css"> | ||
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<!-- Custom CSS --> | ||
<link rel="stylesheet" href="css/app.css"> | ||
<link rel="stylesheet" href="css/bootstrap.min.css"> | ||
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<!-- Clipboard JS --> | ||
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<!-- Custom JS --> | ||
<script src="js/app.js"></script> | ||
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<script> | ||
window.dataLayer = window.dataLayer || []; | ||
function gtag(){dataLayer.push(arguments);} | ||
gtag('js', new Date()); | ||
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gtag('config', 'G-KEX211GQ41'); | ||
</script> | ||
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<!-- Lightbox2 CSS --> | ||
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/lightbox2/2.11.3/css/lightbox.min.css"> | ||
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</head> | ||
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<body> | ||
<div class="container" id="main"> | ||
<div class="row"> | ||
<h2 class="col-md-12 text-center"> | ||
DLT: Conditioned layout generation with Joint Discrete-Continuous Diffusion Layout Transformer </br> | ||
<br> | ||
<small>ICCV 2023</small> | ||
</h2> | ||
</div> | ||
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<br> | ||
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<div class="row"> | ||
<div class="col-md-12 text-center"> | ||
<ul class="list-inline"> | ||
<li><a href="https://www.linkedin.com/in/elad-levi-a938a3121/" target="_blank" rel="noopener noreferrer">Elad Levi</a></li> | ||
<li><a href="https://www.linkedin.com/in/eli-brosh-058989/" target="_blank" rel="noopener noreferrer">Eli Brosh</a></li> | ||
<li><a href="https://www.linkedin.com/in/mykola-mykhailych/" target="_blank" rel="noopener noreferrer">Mykola Mykhailych</a></li> | ||
<li><a href="https://www.linkedin.com/in/meirperez/" target="_blank" rel="noopener noreferrer">Meir Perez</a></li> | ||
</ul> | ||
<strong>WIX.com</strong> | ||
</div> | ||
</div> | ||
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<div class="row"> | ||
<div class="col-md-6 col-md-offset-3 text-center"> | ||
<ul class="nav nav-pills nav-justified"> | ||
<li> | ||
<a href="https://arxiv.org/abs/2303.03755" target="_blank" rel="noopener noreferrer"> | ||
<img src="icon/description.svg" height="50px"> | ||
<h4><strong>Paper</strong></h4> | ||
</a> | ||
</li> | ||
<li> | ||
<a href="" target="_blank" rel="noopener noreferrer"> | ||
<img src="icon/github.png" height="50px"> | ||
<h4><strong>Code (will be released soon)</strong></h4> | ||
</a> | ||
</li> | ||
<li> | ||
<a href="" target="_blank" rel="noopener noreferrer"> | ||
<img src="icon/slideshow.svg" height="50px"> | ||
<h4><strong>Slide (??)</strong></h4> | ||
</a> | ||
</li> | ||
</ul> | ||
</div> | ||
</div> | ||
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<br> | ||
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<div class="row"> | ||
<div class="col-md-8 col-md-offset-2"> | ||
<h3>Abstract</h3> | ||
<p class="text-justify"> | ||
Generating visual layouts is an essential ingredient of graphic design. The ability to condition layout generation on | ||
a partial subset of component attributes is critical to real-world applications that involve user | ||
interaction. Recently, diffusion models have demonstrated high-quality generative performances in | ||
various domains. However, it is unclear how to apply diffusion models to the natural representation | ||
of layouts which consists of a mix of discrete (class) and continuous (location, size) attributes. | ||
To address the conditioning layout generation problem, we introduce DLT, a joint discrete-continuous | ||
diffusion model. DLT is a transformer-based model which has a flexible conditioning mechanism that | ||
allows for conditioning on any given subset of all the layout component classes, locations, and | ||
sizes. Our method outperforms state-of-the-art generative models on various layout generation | ||
datasets with respect to different metrics and conditioning settings. Additionally, we validate the | ||
effectiveness of our proposed conditioning mechanism and the joint continuous-diffusion process. | ||
This joint process can be incorporated into a wide range of mixed discrete-continuous generative | ||
tasks. | ||
</p> | ||
</div> | ||
</div> | ||
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<div class="row"> | ||
<div class="col-md-8 col-md-offset-2"> | ||
<h3>Joint Diffusion process</h3> | ||
<p class="text-justify">The diffusion process is applied jointly on both the continuous attributes of the components (size and location) by adding a small Gaussian noise, and on the discrete attributes of the components (class) by adding a noise that adds a mass to the Mask class. The reverse diffusion model can be conditioned on any given subset of the attributes.</p> | ||
<p class="text-center"> | ||
<a href="./images/diffusion_process4.png" data-lightbox="gallery"> | ||
<img src="./images/diffusion_process4.png" class="img-rounded" width="50%"> | ||
</a> | ||
</p> | ||
</div> | ||
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</div> | ||
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<div class="row"> | ||
<div class="col-md-8 col-md-offset-2"> | ||
<h3>Architecture details</h3> | ||
<p class="text-justify"> | ||
The transformer is fed with an embedding that represents step | ||
<strong><span style="font-size: 16px;">t</span></strong> | ||
of the diffusion process and an embedding for each of the layout components. The output is the | ||
cleaned coordinates and classes of components. During inference, the output is noised back to step | ||
<strong><span style="font-size: 16px;">t-1</span></strong>. | ||
</p> | ||
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<div class="row"> | ||
<a href="./images/model_architecture.png" data-lightbox="gallery"> | ||
<img src="./images/model_architecture.png" class="img-rounded" width="100%"> | ||
</a> | ||
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</div> | ||
<p class="text-justify"> | ||
The component embedding consists of a concatenation of position embedding, size embedding and class | ||
embedding. Each one of the three sub-embeddings can be either generated from the diffusion process | ||
or from the conditioning information. The model is explicitly informed which part of the input is | ||
part of the diffusion process by adding a trainable vector to the sub-embedding in case the | ||
attribute is part of the conditioning. This condition embedding is separate for each attribute. | ||
</p> | ||
<br> | ||
<h3>Results</h3> | ||
<p class="text-justify"> | ||
Qualitative comparison of generated layouts between all tested methods on PubLayNet and Magazine | ||
datasets with the three tested conditioning settings. | ||
</p> | ||
<div class="row"> | ||
<div class="col-md-6"> | ||
<a href="./images/qualitive2.png" data-lightbox="gallery"> | ||
<img src="./images/qualitive2.png" class="img-rounded" width="100%"> | ||
</a> | ||
</div> | ||
<div class="col-md-6"> | ||
<a href="./images/magazine_fig.png" data-lightbox="gallery"> | ||
<img src="./images/magazine_fig.png" class="img-rounded" width="100%"> | ||
</a> | ||
</div> | ||
</div> | ||
<p class="text-justify"> | ||
For more details refer to <a href="https://arxiv.org/abs/2303.08137">our paper</a> and <a href=""> | ||
code</a>. | ||
</p> | ||
</div> | ||
</div> | ||
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<div class="row"> | ||
<div class="col-md-8 col-md-offset-2"> | ||
<h3>Citation</h3> | ||
<div class="form-group col-md-12"> | ||
<pre> | ||
@misc{levi2023dlt, | ||
title={DLT: Conditioned layout generation with Joint Discrete-Continuous Diffusion Layout Transformer}, | ||
author={Elad Levi and Eli Brosh and Mykola Mykhailych and Meir Perez}, | ||
year={2023}, | ||
eprint={2303.03755}, | ||
archivePrefix={arXiv}, | ||
primaryClass={cs.CV} | ||
}</pre> | ||
</div> | ||
</div> | ||
</div> | ||
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<div class="row"> | ||
<div class="col-md-8 col-md-offset-2"> | ||
<h3>Acknowledgements</h3> | ||
<p class="text-justify"> | ||
The website template was borrowed from <a href="https://github.com/jonbarron/website/tree/master/mipnerf">Mip-NeRF 360</a>. | ||
</p> | ||
</div> | ||
</div> | ||
</div> | ||
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© WIX.com<br /> | ||
Last Update: 2023-08-01 | ||
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