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160 changes: 160 additions & 0 deletions css/app.css
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13 changes: 13 additions & 0 deletions css/bootstrap.min.css

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222 changes: 222 additions & 0 deletions index.html
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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">

<!-- Bootstrap CSS -->
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.5/css/bootstrap.min.css">

<!-- Font Awesome CSS -->
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/font-awesome/4.4.0/css/font-awesome.min.css">

<!-- Codemirror CSS -->
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.8.0/codemirror.min.css">

<!-- Custom CSS -->
<link rel="stylesheet" href="css/app.css">
<link rel="stylesheet" href="css/bootstrap.min.css">
<link rel="stylesheet" href="https://fonts.googleapis.com/css2?family=Material+Symbols+Outlined:opsz,wght,FILL,GRAD@48,400,0,0">

<!-- jQuery -->
<script src="https://ajax.googleapis.com/ajax/libs/jquery/1.11.3/jquery.min.js"></script>

<!-- Bootstrap JS -->
<script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.5/js/bootstrap.min.js"></script>

<!-- Codemirror JS -->
<script src="https://cdnjs.cloudflare.com/ajax/libs/codemirror/5.8.0/codemirror.min.js"></script>

<!-- Clipboard JS -->
<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/1.5.3/clipboard.min.js"></script>

<!-- Custom JS -->
<script src="js/app.js"></script>
<script async src="https://www.googletagmanager.com/gtag/js?id=G-KEX211GQ41"></script>

<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());

gtag('config', 'G-KEX211GQ41');
</script>

<!-- Lightbox2 CSS -->
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/lightbox2/2.11.3/css/lightbox.min.css">

</head>

<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>

<br>

<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>

<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>

<br>

<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>

<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>

</div>

<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>

<div class="row">
<a href="./images/model_architecture.png" data-lightbox="gallery">
<img src="./images/model_architecture.png" class="img-rounded" width="100%">
</a>

</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>

<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>

<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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Last Update: 2023-08-01
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