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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" -->
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<meta name="description" content="DESCRIPTION META TAG">
<meta property="og:title" content="svi-diffusion"/>
<meta property="og:description" content="Project Page for svi-diffusion"/>
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/>
<style>
.center-item {
display: flex;
justify-content: center;
align-items: center;
}
.eps_null {
color: rgb(53, 144, 243);
}
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color: rgb(231, 111, 81);
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color: rgb(42, 157, 143);
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border-top: 2px solid #bbb;
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table, td, th {
border: 1px solid black;
}
table {
border-collapse: collapse;
width: 100%;
}
td {
text-align: center;
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}
</style>
</head>
<body>
<section class="hero">
<div class="hero-body">
<div class="container is-max-desktop">
<div class="columns is-centered">
<div class="column has-text-centered">
<h1 class="title is-1 publication-title"><span class="video-timedeconv">Solving Video Inverse Problems Using Image Diffusion Models</h1>
<div class="is-size-4 publication-authors">
<!-- Paper authors -->
<span class="author-block">
<a href="https://star-kwon.github.io/" target="_blank">Taesung Kwon</a>,</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=HNMjoNEAAAAJ&hl=en" target="_blank">Jong Chul Ye</a></span>
</div>
<div class="is-size-4 publication-authors">
<span class="author-block" style="color:black"> ICLR 2025 </span>
<!-- <br>Conferance name and year</span> -->
<!-- <span class="conference-name" style="color:black">ICLR 2025</span> -->
</div>
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class="external-link button is-normal is-rounded is-dark">
</ARXIV>
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</span>
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</a>
</span> -->
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<a href="https://arxiv.org/abs/2409.02574" target="_blank"
class="external-link button is-normal is-rounded is-dark">
</ARXIV>
<span class="icon">
<i class="ai ai-arxiv"></i>
</span>
<span>arXiv</span>
</a>
</span>
<!-- Github link -->
<span class="link-block">
<a href="https://github.com/svi-diffusion/codes" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fab fa-github"></i>
</span>
<span>Code</span>
</a>
</span>
</div>
</div>
</div>
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</section>
<!-- Teaser Carousel-->
<section class="hero teaser">
<div class="columns is-centered" style="margin-top:-20px">
<h1 class="is-size-3">
<mark style="background: rgb(255, 255, 102)"> Solving video inverse problems using only image diffusion models,
</div>
<div class="columns is-centered" style="margin-top:-10px">
<h1 class="is-size-3">
<mark style="background: rgb(255, 255, 102)"> with batch-consistent sampling stretagy.
</mark></h1>
</div>
<div class="hero-body">
<div class="container">
<div class="columns is-centered" style="margin-top:20px">
<h1 class="is-size-4"> Reconstruction Results for temporal degradations</h1>
<h1 class="is-size-5", style="margin-top:4px"> (256x256 ADM) </h1>
</div>
<div id="results-carousel" class="carousel results-carousel" style="margin-top:-25px">
<div class="item">
<!-- Your image here -->
<div class="center-item">
<img src="static/images/13_1.gif" alt="MY ALT TEXT"/>
</div>
<h2 class="subtitle has-text-centered">
“16-frames 256x256 13-frame averaged”
</h2>
</div>
<div class="item">
<!-- Your image here -->
<div class="center-item">
<img src="static/images/13_2.gif" alt="MY ALT TEXT"/>
</div>
<h2 class="subtitle has-text-centered">
“16-frames 256x256 13-frame averaged”
</h2>
</div>
<div class="item">
<!-- Your image here -->
<div class="center-item">
<img src="static/images/13_3.gif" alt="MY ALT TEXT"/>
</div>
<h2 class="subtitle has-text-centered">
“16-frames 256x256 13-frame averaged”
</h2>
</div>
<div class="item">
<!-- Your image here -->
<div class="center-item">
<img src="static/images/13_4.gif" alt="MY ALT TEXT"/>
</div>
<h2 class="subtitle has-text-centered">
“16-frames 256x256 13-frame averaged”
</h2>
</div>
<div class="item">
<!-- Your image here -->
<div class="center-item">
<img src="static/images/7_1.gif" alt="MY ALT TEXT"/>
</div>
<h2 class="subtitle has-text-centered">
“16-frames 256x256 7-frame averaged”
</h2>
</div>
<div class="item">
<!-- Your image here -->
<div class="center-item">
<img src="static/images/7_2.gif" alt="MY ALT TEXT"/>
</div>
<h2 class="subtitle has-text-centered">
“16-frames 256x256 7-frame averaged”
</h2>
</div>
<div class="item">
<!-- Your image here -->
<div class="center-item">
<img src="static/images/7_3.gif" alt="MY ALT TEXT"/>
</div>
<h2 class="subtitle has-text-centered">
“16-frames 256x256 7-frame averaged”
</h2>
</div>
<div class="item">
<!-- Your image here -->
<div class="center-item">
<img src="static/images/7_4.gif" alt="MY ALT TEXT"/>
</div>
<h2 class="subtitle has-text-centered">
“16-frames 256x256 7-frame averaged”
</h2>
</div>
<div class="item">
<!-- Your image here -->
<div class="center-item">
<img src="static/images/g7_1.gif" alt="MY ALT TEXT"/>
</div>
<h2 class="subtitle has-text-centered">
“16-frames 256x256 convolved with Gaussian PSF”
</h2>
</div>
<div class="item">
<!-- Your image here -->
<div class="center-item">
<img src="static/images/g7_2.gif" alt="MY ALT TEXT"/>
</div>
<h2 class="subtitle has-text-centered">
“16-frames 256x256 convolved with Gaussian PSF”
</h2>
</div>
<div class="item">
<!-- Your image here -->
<div class="center-item">
<img src="static/images/g7_3.gif" alt="MY ALT TEXT"/>
</div>
<h2 class="subtitle has-text-centered">
“16-frames 256x256 convolved with Gaussian PSF”
</h2>
</div>
</div>
</div>
</section>
<!-- End teaser video -->
<!-- Paper abstract -->
<section class="section hero is-light">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-sixths">
<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p style="font-size:18px;">
Diffusion model-based inverse problem solvers (DIS) have emerged as state-of-the-art approaches for addressing inverse problems.
However, their application to video inverse problems arising from spatio-temporal degradation remains largely unexplored.
In response, we introduce an innovative video inverse solver using only image diffusion models.
Specifically, our method treats the <strong> time </strong> dimension of a video as the <strong> batch </strong> dimension of image diffusion models,
thereby solving spatio-temporal optimization problems within denoised spatio-temporal batches derived from each image diffusion model.
We address the <strong> batch inconsistency </strong> issue in diffusion models by controlling batch-stochasticity,
thereby enabling <strong> batch-consistent sampling</strong>.
<br>
<table style="width: 80%; margin-left:auto; margin-right:auto; text-align: center;
margin-top:20px; margin-bottom:20px; background-color: rgb(245, 242, 237);" border="3" cellpadding="3">
<tbody>
<tr>
<td style="width: 30%; font-size: 22px;"></td>
<td style="width: 50%; font-size: 22px;"><strong>Ours 😁 </strong></td>
</tr>
<tr>
<td style="width: 30%; font-size: 20px; vertical-align:middle;">Video reconstruction</td>
<td style="width: 50%; font-size: 20px; vertical-align:middle;">Better Reconstruction Quality <br> & Batch-Consistent Samples</td>
</tr>
<tr>
<td style="width: 30%; font-size: 20px; vertical-align:middle;">Memory effieciency</td>
<td style="width: 50%; font-size: 20px; vertical-align:middle;">Requires 13GB VRAM for 16-frame videos, maximum 32-frame in 24 GB VRAM.</td>
</tr>
<tr>
<td style="width: 30%; font-size: 20px; vertical-align:middle;">Accessibility</td>
<td style="width: 50%; font-size: 20px; vertical-align:middle;">Using open-sourced image diffusion model (ADM)</td>
</tr>
</tbody>
</table>
Experimental results demonstrate that our method effectively addresses various temporal and spatial degradations in video inverse problems,
achieving state-of-the-art reconstructions.
</p>
</div>
</div>
</div>
</div>
</section>
<!-- End paper abstract -->
<!-- Batch reconstruction -->
<section class="section hero">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-sixths">
<h2 class="title is-3">Batch-consistent Sampling Stretagy</h2>
<div class="content has-text-justified">
<div class="center-item">
<img src="static/images/1.png" alt="1" style="width: 80%"/>
</div>
<p style=" font-size:18px;">
Geometric illustration of the sampling path evolution.
(a) Common batch sampling in image diffusion models is batch-independent.
(b) In standard batch-consistent sampling, identical images are sampled by synchronizing the stochastic noise components.
(c) Frame-dependent perturbation through multi-step CG ensures that the batch-consistent sampling meets the spatio-temporal data consistency.
</p>
</div>
</div>
</div>
</div>
</section>
<!-- End Batch reconstruction -->
<!-- Method -->
<section class="section hero is-light">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-sixths">
<h2 class="title is-3">Method</h2>
<div class="content has-text-justified">
<img src="static/images/2.png" alt="2"/>
<p style=" font-size:18px;">
Illustration of the intermediate sampling process in our video inverse problem solver that leverages only image diffusion models.
By taking <strong> multi-step CG </strong> in the video space of the Tweedie denoised manifold, we iteratively solve the video inverse problem.
We synchronize the <strong> stochastic noise components </strong> in the reverse diffusion process
to ensure <strong> batch-consistent sampling </strong> with state-of-the-art reconstruction performance.
</p>
</div>
</div>
</div>
</div>
</section>
<!-- End Method -->
<!-- Results -->
<section class="section hero">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-sixths">
<h2 class="title is-3">Experimental results</h2>
<div class="content has-text-justified">
<p style=" font-size:18px;">
As demonstrated by the teaser videos, we successfully solved temporal degradations with various PSFs.
Even with the additional spatial degradations,
our method demonstrates robustness against various combinations of spatio-temporal degradations.
</p>
</div>
</div>
</div>
</div>
</section>
<!-- End Results -->
<!-- Teaser Carousel-->
<section class="hero teaser">
<div class="hero-body">
<div class="container">
<div class="columns is-centered" style="margin-top:20px">
<h1 class="is-size-4"> Reconstruction Results for spatio-temporal degradations</h1>
<h1 class="is-size-5", style="margin-top:4px"> (256x256 ADM) </h1>
</div>
<div id="results-carousel" class="carousel results-carousel" style="margin-top:-25px">
<div class="item">
<!-- Your image here -->
<div class="center-item">
<img src="static/images/sr_1.gif" alt="MY ALT TEXT"/>
</div>
<h2 class="subtitle has-text-centered">
“SRx4 & 7-frame averaged”
</h2>
</div>
<div class="item">
<!-- Your image here -->
<div class="center-item">
<img src="static/images/sr_2.gif" alt="MY ALT TEXT"/>
</div>
<h2 class="subtitle has-text-centered">
“SRx4 & 7-frame averaged”
</h2>
</div>
<div class="item">
<!-- Your image here -->
<div class="center-item">
<img src="static/images/sr_3.gif" alt="MY ALT TEXT"/>
</div>
<h2 class="subtitle has-text-centered">
“SRx4 & 7-frame averaged”
</h2>
</div>
<div class="item">
<!-- Your image here -->
<div class="center-item">
<img src="static/images/deblur_1.gif" alt="MY ALT TEXT"/>
</div>
<h2 class="subtitle has-text-centered">
“Deblur (σ = 2.0) & 7-frame averaged”
</h2>
</div>
<div class="item">
<!-- Your image here -->
<div class="center-item">
<img src="static/images/deblur_2.gif" alt="MY ALT TEXT"/>
</div>
<h2 class="subtitle has-text-centered">
“Deblur (σ = 2.0) & 7-frame averaged”
</h2>
</div>
<div class="item">
<!-- Your image here -->
<div class="center-item">
<img src="static/images/deblur_3.gif" alt="MY ALT TEXT"/>
</div>
<h2 class="subtitle has-text-centered">
“Deblur (σ = 2.0) & 7-frame averaged”
</h2>
</div>
<div class="item">
<!-- Your image here -->
<div class="center-item">
<img src="static/images/deblur_4.gif" alt="MY ALT TEXT"/>
</div>
<h2 class="subtitle has-text-centered">
“Deblur (σ = 2.0) & 7-frame averaged”
</h2>
</div>
<div class="item">
<!-- Your image here -->
<div class="center-item">
<img src="static/images/inpaint_1.gif" alt="MY ALT TEXT"/>
</div>
<h2 class="subtitle has-text-centered">
“Inpaint (50%) & 7-frame averaged”
</h2>
</div>
<div class="item">
<!-- Your image here -->
<div class="center-item">
<img src="static/images/inpaint_2.gif" alt="MY ALT TEXT"/>
</div>
<h2 class="subtitle has-text-centered">
“Inpaint (50%) & 7-frame averaged”
</h2>
</div>
<div class="item">
<!-- Your image here -->
<div class="center-item">
<img src="static/images/inpaint_3.gif" alt="MY ALT TEXT"/>
</div>
<h2 class="subtitle has-text-centered">
“Inpaint (50%) & 7-frame averaged”
</h2>
</div>
<div class="item">
<!-- Your image here -->
<div class="center-item">
<img src="static/images/inpaint_4.gif" alt="MY ALT TEXT"/>
</div>
<h2 class="subtitle has-text-centered">
“Inpaint (50%) & 7-frame averaged”
</h2>
</div>
</div>
</div>
</section>
<!-- End teaser video -->
<!-- Visualization -->
<section class="section hero is-light">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-sixths">
<h2 class="title is-3">Visualization with Comparative Methods</h2>
<div class="content has-text-justified">
<div class="center-item">
<img src="static/images/video_results.gif" alt="2"/>
</div>
<p style=" font-size:18px;">
We provide visualization of reconstruction results with comparative methods in video format.
We also provide ablation study results.
</p>
</div>
</div>
</div>
</div>
</section>
<!-- End Visualization -->
<!--BibTex citation -->
<!-- <section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>BibTex Code Here</code></pre>
</div>
</section> -->
<!--End BibTex citation -->
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