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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="description"
content="RMTF">
<meta name="keywords" content="Text-to-Video, Diffusion Model">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>RDTF</title>
<script type="text/x-mathjax-config">
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</script>
<script type="text/javascript"
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</script>
<link href="https://fonts.googleapis.com/css?family=Google+Sans|Noto+Sans|Castoro" rel="stylesheet">
<link rel="stylesheet" href="./static/css/bulma.min.css">
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<script src="./static/js/bulma-slider.min.js"></script>
<script src="./static/js/index.js"></script>
</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">
<h2 class="title is-2 publication-title">
<img class="center" width="100" height="35" src="assets/RDTF.png">
Resource-Efficient Dual-Mask Training Framework for Animated Sticker Generation
</h2>
<div class="is-size-5 publication-authors">
<div class="column has-text-centered">
<div class="publication-links">
<span class="link-block">
<a href=""
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="ai ai-arxiv"></i>
</span>
<span>arXiv</span>
</a>
</span>
<span class="link-block">
<a href="https://sprproxy-1258344707.cos.ap-shanghai.myqcloud.com/seraphyuan/ilabel/vsd2m_full.zip"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fab fa-github"></i>
</span>
<span>Data (1.3T)</span>
</a>
</span>
<span class="link-block">
<a href="https://github.com/mails9523/mails9523.github.io"
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>
<!--
<span class="author-block">
<a href="https://chenhsing.github.io">Zhen Xing</a><sup>1</sup>,</span>
<span class="author-block">
Qi Dai<sup>2</sup>,</span>
<span class="author-block">
Han Hu<sup>2</sup>,
</span>
<span class="author-block">
Zuxuan Wu<sup>1</sup>,
</span>
<span class="author-block">
Yu-Gang Jiang<sup>1</sup>
</span>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup> Fudan University,</span>
<span class="author-block"><sup>2</sup>MicroSoft Research Asia</span>
</div>
<div class="column has-text-centered">
<div class="publication-links">
<span class="link-block">
<a href="https://arxiv.org/abs/2308.09710"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fas fa-file-pdf"></i>
</span>
<span>Paper</span>
</a>
</span>
<span class="link-block">
<a href="https://arxiv.org/abs/2308.09710"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="ai ai-arxiv"></i>
</span>
<span>arXiv</span>
</a>
</span>
<span class="link-block">
<a href="https://github.com/ChenHsing/SimDA"
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>
</div>
</div>
</div>
</section>
<section class="section">
<div class="container is-max-desktop">
<!-- Abstract. -->
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
Recently, great progress has been made in video generation technology, attracting the widespread attention of scholars.
To apply this technology to downstream applications under resource-constrained conditions, researchers often fine-tune pre-trained models based on parameter-efficient tuning methods such as Adapter or Lora.
Although these methods can transfer source domain knowledge to the target domain, fewer training parameters lead to poor fitting ability, and the source domain knowledge may cause the inference process to deviate from the target domain.
</p>
<p>
In this paper, we argue that under limited computing resources, training a smaller video generation model from scratch using only million-level samples can outperform parameter-efficient tuning on larger models in downstream applications: the core lies in the <i>effective utilize of data</i> and <i>curriculum strategy</i> .
Take animated sticker generation (ASG) as an example, we construct a discrete frame generation network for stickers with low frame rates, ensuring that its parameters meet the requirements of model training under limited resources.
In order to provide data support for models trained from scratch, we come up with a dual-mask based data utilization strategy, which manages to improve the availability and expand the diversity of limited data.
To facilitate convergence under dual-mask situation, we propose a difficulty-adaptive curriculum learning method, which decomposes the sample entropy into static and adaptive components so as to obtain samples from easy to difficult.
The proposed resource-efficient dual-mask training framework is quantitatively and qualitatively superior to efficient-parameter tuning methods such as I2V-Adapter and SimDA on the ASG task, verifying the feasibility of our method on downstream tasks under limited resources.
</p>
</div>
</div>
</div>
<!--/ Abstract. -->
<section class="section" id="methods">
<hr />
<h2 class="title is-3">Method</h2>
<hr />
<td align="center" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="1280"
height="160" src="assets/fig1.jpg"></td>
<center>
<h5 class="title is-6">Exemplary results of animated sticker generation using our proposed approach, RDTF.
Our method performs well on different generation tasks: (top) Text&image to GIF, (middle) Prediction, (down) Interpolation.
Gray boxes indicate text or visual guidance.</h5>
</center>
<hr />
<td align="center" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="1280"
height="160" src="assets/overview.jpg"></td>
<center>
<h5 class="title is-6">An overview of resource-efficient multi-task training framework.
We adopt a model size that can be tolerated under constrained resources and propose a spatio temporal interaction layer to model the discreteness between animated stickers.
Furthermore, the condition mask and loss mask are designed to enable the model can effectively utilize data through multi-task and short-frame data learning, and the curriculum learning is applied to improve training of resource-constrained model.</h5>
</center>
</section>
<section class="section" id="results">
<hr />
<h2 class="title is-3">Result Visualization</h2>
<table align="center">
<div class="center-text">
</div>
<br>
<tr>
<td width="80" valign="middle" halign="middle" align="middle"
style="font-size:13px; padding-bottom:20px">Inputs:</td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="1280"
height="160" src="assets/interpolation/b279f3612337f6116d365c36ee48925e_ref.png"></td>
</tr>
<tr>
<td width="80" valign="middle" halign="middle" align="middle"
style="font-size:13px; padding-bottom:20px">Outputs:</td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="1280"
height="160" src="assets/interpolation/b279f3612337f6116d365c36ee48925e_pred.png"></td>
</tr>
<tr>
<td width="80" valign="middle" halign="middle" align="middle"
style="font-size:13px; padding-bottom:20px">Inputs:</td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="1280"
height="160" src="assets/interpolation/632bec5e08f177cd9f37a353697b6626_ref.png"></td>
</tr>
<tr>
<td width="80" valign="middle" halign="middle" align="middle"
style="font-size:13px; padding-bottom:20px">Outputs:</td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="1280"
height="160" src="assets/interpolation/632bec5e08f177cd9f37a353697b6626_pred.png"></td>
</tr>
</table>
<center>
<h3 class="title is-4">Interpolation</h3>
</center>
</table>
<table align="center">
<div class="large-4 columns">
</div>
<br>
<tr>
<td width="80" valign="middle" halign="middle" align="middle"
style="font-size:13px; padding-bottom:20px">Inputs:</td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="1280"
height="160" src="assets/frame_prediction/191ebc1fe1716b012caf75913c0bb0e9_ref.png"></td>
</tr>
<tr>
<td width="80" valign="middle" halign="middle" align="middle"
style="font-size:13px; padding-bottom:20px">Outputs:</td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="1280"
height="160" src="assets/frame_prediction/191ebc1fe1716b012caf75913c0bb0e9_pred.png"></td>
</tr>
<tr>
<td width="80" valign="middle" halign="middle" align="middle"
style="font-size:13px; padding-bottom:20px">Inputs:</td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="1280"
height="160" src="assets/frame_prediction/26109fcae83c64b6d77329e908f10bb4_ref.png"></td>
</tr>
<tr>
<td width="80" valign="middle" halign="middle" align="middle"
style="font-size:13px; padding-bottom:20px">Outputs:</td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="1280"
height="160" src="assets/frame_prediction/26109fcae83c64b6d77329e908f10bb4_pred.png"></td>
</tr>
</table>
<center>
<h3 class="title is-4">Pre/Post Frame Prediction</h3>
</center>
</table>
<table align="center">
<div class="large-4 columns">
</div>
<br>
<tr>
<td width="80" valign="middle" halign="middle" align="middle"
style="font-size:13px; padding-bottom:20px">Input Text:</td>
<td width="1280" valign="middle" halign="left" align="left"
style="font-size:13px; padding-bottom:20px"> <i>A small cartoon character pointing at a heart </i></td>
</tr>
<tr>
<td width="80" valign="middle" halign="middle" align="middle"
style="font-size:13px; padding-bottom:20px">Ref Img:</td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="1280"
height="160" src="assets/it2v/9b4cdab649a2fcc7b2411f669414e540_ref.png"></td>
</tr>
<tr>
<td width="80" valign="middle" halign="middle" align="middle"
style="font-size:13px; padding-bottom:20px">Outputs:</td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="1280"
height="160" src="assets/it2v/9b4cdab649a2fcc7b2411f669414e540_pred.png"></td>
</tr>
<tr>
<td width="80" valign="middle" halign="middle" align="middle"
style="font-size:13px; padding-bottom:20px">Input Text:</td>
<td width="240" valign="middle" halign="left" align="left"
style="font-size:13px; padding-bottom:20px"> <i>A cute white cartoon teddy bear holding a heart in his hands</i></td>
</tr>
<tr>
<td width="80" valign="middle" halign="middle" align="middle"
style="font-size:13px; padding-bottom:20px">Ref Img:</td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="1280"
height="160" src="assets/it2v/b3726e12426a3b9100b42e33c294375f_ref.png"></td>
</tr>
<tr>
<td width="80" valign="middle" halign="middle" align="middle"
style="font-size:13px; padding-bottom:20px">Outputs:</td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="1280"
height="160" src="assets/it2v/b3726e12426a3b9100b42e33c294375f_pred.png"></td>
</tr>
</table>
<center>
<h3 class="title is-4">Image&Text to GIF</h3>
</center>
</center>
</section>
<section class="section" id="results">
<hr />
<h2 class="title is-3">Compare to Other Methods</h2>
</center>
<table align="center">
<div class="center-text">
</div>
<br>
<tr>
<td width="145" valign="middle" halign="middle" align="center"
style="font-size:14px"><b>Ref Image</b></td>
<td width="145" valign="middle" halign="middle" align="center"
style="font-size:14px"><b>Ref Text</b></td>
<td width="145" valign="middle" halign="middle" align="center"
style="font-size:14px"><b>Customize-A-Video [1]</b></td>
<td width="145" valign="middle" halign="middle" align="center"
style="font-size:14px"><b>I2V-Adapter [2]</b></td>
<td width="145" valign="middle" halign="middle" align="center"
style="font-size:14px"><b>SimDA [3]</b></td>
<td width="145" valign="middle" halign="middle" align="center"
style="font-size:14px"><b>Ours</b></td>
</tr>
<tr>
<td align="center" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="145"
height="145" src="assets/comp/case1_ref.gif"></td>
<td width="145" valign="middle" halign="middle" align="center"
style="font-size:14px">A cute little girl in a pink bunny hat showing surprise and joy</td>
<td align="center" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="145"
height="145" src="assets/comp/case1_cav.gif"></td>
<td align="center" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="145"
height="145" src="assets/comp/case1_i2vadapter.gif"></td>
<td align="center" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="145"
height="145" src="assets/comp/case1_simda.gif"></td>
<td align="center" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="145"
height="145" src="assets/comp/case1_our.gif"></td>
</tr>
<tr>
<td align="center" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="145"
height="145" src="assets/comp/case2_ref.gif"></td>
<td width="145" valign="middle" halign="middle" align="center"
style="font-size:14px">A small cartoon character pointing at a heart</td>
<td align="center" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="145"
height="145" src="assets/comp/case2_cav.gif"></td>
<td align="center" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="145"
height="145" src="assets/comp/case2_i2vadapter.gif"></td>
<td align="center" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="145"
height="145" src="assets/comp/case2_simda.gif"></td>
<td align="center" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="145"
height="145" src="assets/comp/case2_our.gif"></td>
</tr>
<tr>
<td align="center" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="145"
height="145" src="assets/comp/case3_ref.gif"></td>
<td width="145" valign="middle" halign="middle" align="center"
style="font-size:14px">A yellow smiley face with two thumbs up, symbolizing approval, satisfaction or enthusiasm</td>
<td align="center" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="145"
height="145" src="assets/comp/case3_cav.gif"></td>
<td align="center" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="145"
height="145" src="assets/comp/case3_i2vadapter.gif"></td>
<td align="center" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="145"
height="145" src="assets/comp/case3_simda.gif"></td>
<td align="center" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="145"
height="145" src="assets/comp/case3_our.gif"></td>
</tr>
</table>
</section>
<section class="section" id="results">
<hr />
<h2 class="title is-3">More Results</h2>
<table align="center">
<div class="large-4 columns">
</div>
<br>
<tr>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256" src="assets/interpolation/104ea41e839d7a174382e757cbf4d889_pred.gif"></td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256" src="assets/interpolation/a303ff495758354f4fc9a4b91ab8723c_pred.gif"></td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256" src="assets/interpolation/a43414dd76ae3577bb4ba6ccd1ec7e4c_pred.gif"></td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256"
src="assets/interpolation/b279f3612337f6116d365c36ee48925e_pred.gif">
</td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256" src="assets/interpolation/b3726e12426a3b9100b42e33c294375f_pred.gif"></td>
</tr>
<tr>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256" src="assets/interpolation/bb1a82dc9e5791bd5a299a6e306b5051_pred.gif"></td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256" src="assets/interpolation/632bec5e08f177cd9f37a353697b6626_pred.gif"></td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256" src="assets/interpolation/071ada92e48b3b6beb89e298074aed5b_pred.gif"></td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256"
src="assets/interpolation/44d41d2863c3fd90ffb47f16d4f0931c_pred.gif">
</td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256" src="assets/interpolation/4d0f81ff7a7fa9f312cb96b01007c7a4_pred.gif"></td>
</tr>
<tr>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256" src="assets/interpolation/4bf84d5ad334f785f198a76766a25025_pred.gif"></td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256" src="assets/interpolation/827589bfd6d21f29d71013c657023207_pred.gif"></td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256" src="assets/interpolation/5fdc05f233517d9aad9b15d8006cc833_pred.gif"></td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256" src="assets/interpolation/89757532a98a8a8aed76640a38045478_pred.gif"></td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256" src="assets/interpolation/cc5370973067744a6fb7d83e73725104_pred.gif"></td>
</tr>
</table>
<center>
<h3 class="title is-4">Interpolation</h3>
</center>
</table>
<table align="center">
<div class="large-4 columns">
</div>
<br>
<tr>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256" src="assets/frame_prediction/2af4c8b73e303e3ecc83c1cef90e9243_pred.gif"></td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256" src="assets/frame_prediction/2f940921641b4688c89b6cb06ec58cfa_pred.gif"></td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256" src="assets/frame_prediction/5cac5ac1834f4e8f14230b366cc2c0fe_pred.gif"></td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256"
src="assets/frame_prediction/191ebc1fe1716b012caf75913c0bb0e9_pred.gif">
</td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256"
src="assets/frame_prediction/5f3c66570b1b5101f3053e8157d20709_pred.gif">
</td>
</tr>
<tr>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256" src="assets/frame_prediction/26109fcae83c64b6d77329e908f10bb4_pred.gif"></td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256" src="assets/frame_prediction/4621f037f8ff6409657d45e792172f42_pred.gif"></td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256" src="assets/frame_prediction/480419f7acc9acc2f46a88934490eed6_pred.gif"></td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256" src="assets/frame_prediction/2b160f995509b749f266dbddad18e115_pred.gif"></td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256" src="assets/frame_prediction/8e7c4ba5bfaf3b84bb0cb946da022f87_pred.gif"></td>
</tr>
<tr>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256" src="assets/frame_prediction/c1fb36426ca655f1766669c1879b588c_pred.gif"></td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256" src="assets/frame_prediction/cc5370973067744a6fb7d83e73725104_pred.gif"></td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256"
src="assets/frame_prediction/e3c7610baccf21c2ff1337d2d7ec0715_pred.gif">
</td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256" src="assets/frame_prediction/f1a7af33eb110f9799a21f46a1014a22_pred.gif"></td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256" src="assets/frame_prediction/0840d548f8091e4f3e73ad391b89fcd6_pred.gif"></td>
</tr>
</table>
<center>
<h3 class="title is-4">Prediction</h3>
</center>
</table>
<table align="center">
<div class="large-4 columns">
</div>
<br>
<tr>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256" src="assets/it2v/cb572e531ac78bddbeccb94c33b8fc83_pred.gif"></td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256" src="assets/it2v/9b4cdab649a2fcc7b2411f669414e540_pred.gif"></td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256" src="assets/it2v/329865e5a656a70850ea8a43a78e0467_pred.gif"></td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256" src="assets/it2v/0ee0400d8437415f3dbc35ee40cb9678_pred.gif"></td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="240"
height="256" src="assets/it2v/7704813bc3914f9dbb6f0c5021e8ca8d_pred.gif"></td>
</tr>
<tr>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256" src="assets/it2v/27a47d68a1a98f18c63982e2040029a5_pred.gif"></td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256" src="assets/it2v/b26a618780c5b5442be0cd0f75e5898b_pred.gif"></td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256" src="assets/it2v/3bf1da9f274cace37564aeb88a43171d_pred.gif"></td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256" src="assets/it2v/337e01b404fe89993de375286e4e4b07_pred.gif"></td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256" src="assets/it2v/b63432fbc1ea49da1e31175b3724d210_pred.gif"></td>
</tr>
<tr>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256" src="assets/it2v/c9d58bb9264f1efd0cf17c07dba1da19_pred.gif"></td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256" src="assets/it2v/5cac5ac1834f4e8f14230b366cc2c0fe_pred.gif"></td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256" src="assets/it2v/0b0f4f4033e3e85f95b13d2d1af55ac5_pred.gif"></td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256" src="assets/it2v/71eb060ffacfd25d589e09774e76a2de_pred.gif"></td>
<td align="left" style="padding-left: 0px; padding-bottom: 0px;"><img class="center" width="256"
height="256" src="assets/it2v/595e150c2b0b83d806b19f12e03ba582_pred.gif"></td>
</tr>
</table>
<center>
<h3 class="title is-4">Generation</h3>
</center>
</center>
</section>
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<p> If you use our work in your research, please cite: </p>
<pre><code>
@misc{anonymous2025RDTF,
title={Resource-Efficient Dual-Mask Training Framework for Animated Sticker Generation},
author={Anonymous},
archivePrefix={arXiv},
primaryClass={cs.CV}}
</code></pre>
</div>
</section>
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<p>[1] Jonathan Ho, Tim Salimans, Alexey A Gritsenko, William Chan, Mohammad Norouzi, and David
J Fleet. Video
diffusion models. In
ICLR, 2022.</p>
<p>[2] Wenyi Hong, Ming Ding, Wendi Zheng, Xinghan Liu, and Jie Tang. Cogvideo: Large-scale
pretraining for
text-to-video generation
via transformers. In ICLR, 2023.</p>
<p>[3] Luo, Zhengxiong and Chen, Dayou and Zhang, Yingya and Huang, Yan and Wang, Liang and Shen, Yujun and Zhao, Deli and Zhou, Jingren and Tan, Tieniu
VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation. In CVPR, 2023.</p>
<p>[4] He, Yingqing, et al. "Latent video diffusion models for high-fidelity video generation with arbitrary lengths." arXiv preprint(2022).</p>
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<p>
Tempolate from <a href="https://github.com/nerfies/nerfies.github.io">Nerfies</a>, thanks!
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<p>[1] Ren, Y., Zhou, Y., Yang, J., Shi, J., Liu, D., Liu, F., ... & Shrivastava, A. (2024). Customize-A-Video: One-Shot Motion Customization of Text-to-Video Diffusion Models. CoRR.</p>
<p>[2] Guo, X., Zheng, M., Hou, L., Gao, Y., Deng, Y., Wan, P., ... & Ma, C. (2024, July). I2V-Adapter: A General Image-to-Video Adapter for Diffusion Models. In ACM SIGGRAPH 2024 Conference Papers (pp. 1-12).</p>
<p>[3] Xing, Z., Dai, Q., Hu, H., Wu, Z., & Jiang, Y. G. (2024). Simda: Simple diffusion adapter for efficient video generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 7827-7839).</p>
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