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<section class="hero">
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<div class="container is-max-desktop">
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<h1 class="title is-1 publication-title">DyNCA: Real-Time Dynamic Texture Synthesis Using Neural
Cellular Automata</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://scholar.google.com/citations?hl=en&user=bddQ6pQAAAAJ">Ehsan Pajouheshgar</a><sup>*</sup>,</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=jvCvNPQAAAAJ&hl=en/">Yitao Xu</a><sup>*</sup>,</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=kCy8JG8AAAAJ&hl=en">Tong Zhang</a>,</span>
<span class="author-block">
<a href="https://www.epfl.ch/labs/ivrl/">Sabine Süsstrunk</a></span>
</div>
<div class="is-size-6 publication-authors">
<span class="author-block">Image and Visual Representation Lab (IVRL)</span>
</div>
<!-- <div class="is-size-5 publication-authors">-->
<!-- <span class="author-block">School of Computer and Communication Sciences (IC)</span>-->
<!-- </div>-->
<div class="is-size-6 publication-authors">
<span class="author-block">École Polytechnique Fédérale de Lausanne (EPFL)</span>
</div>
<div class="is-size-4 publication-authors">
<span class="author-block">CVPR 2023 Conference</span>
</div>
<div class="column has-text-centered">
<div class="publication-links">
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<a href="https://arxiv.org/abs/2211.11417"
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>
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<a href="supplementary/"
class="external-link button is-normal is-rounded is-dark">
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<i class="fas fa-folder"></i>
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<span>Supplementary</span>
</a>
</span>
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class="external-link button is-normal is-rounded is-dark">
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<span>Video</span>
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<a href="https://github.com/IVRL/DyNCA"
class="external-link button is-normal is-rounded is-dark">
<span class="icon"><i class="fab fa-github"></i></span>
<span>GitHub</span>
</a>
</span>
<span class="link-block">
<a href="https://colab.research.google.com/github/IVRL/DyNCA/blob/main/notebooks/vector_field_motion_colab.ipynb"
class="external-link button is-normal is-rounded is-dark">
<span class="icon"><i class="fab fa-google"></i></span>
<span>Google Colab</span>
</a>
</span>
</div>
</div>
</div>
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</div>
</div>
</section>
<section class="section is-light">
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<i class="fas fa-home"></i>
</span>
</a>
<div class="navbar-item has-dropdown is-hoverable">
<a class="navbar-link" id="select_demo_type">
Demo Type: Vector Field Motion
</a>
<div class="navbar-dropdown">
<a class="navbar-item" id="exp_type_vector_motion"> Motion from a Vector Field</a>
<a class="navbar-item" id="exp_type_video_motion"> Motion From a Video</a>
</div>
</div>
</div>
<div class="columns is-centered has-text-centered">
</div>
</div>
</section>
<section class="hero teaser">
<div class="container is-max-desktop">
<div class="hero-body">
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<div>
<canvas id="demo-canvas"></canvas>
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</span>
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<use xlink:href="#resetIcon"></use>
</svg>
<label><img id="benchmark"
src="images/benchmark.png" width="50px" height="50px"/></label>
</div>
<div class="speed">
<input type="range" id="speed" min="-4" max="4" step="1" value="0"></br>Steps / frame: <label
id="speedLabel"></label>
</div>
<div style="text-align: right;">
<span id="record">
<img id="record_off" src="images/record_off.png" width="40px" height="40px"
style="padding: 4px; padding-bottom: 2px"/>
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style="padding: 4px; padding-bottom: 2px; display: none;"/>
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</svg>
</div>
<div id='brushSelect' class="button-group">
<label>
<input type="radio" name="brush"><img
src="images/brush_S.png" width="38px" height="38px"/>
</label>
<label>
<input type="radio" name="brush"><img src="images/brush_M.png" width="38px"
height="38px"/>
</label>
<label>
<input type="radio" name="brush" checked="checked"><img
src="images/brush_L.png" width="38px" height="38px"/>
</label>
<br> Brush Size
</div>
<div>
<input type="range" id="rotation" min="0" max="360" step="1" value="0"></br>Rotation: <label
id="rotationLabel"></label><br>
<!-- <input type="range" id="rate" min="0.01" max="1.0" step="0.01" value="1.0"></br>Speed: <label id="rateLabel"></label><br>-->
</div>
<div id='configSelect' class="button-group">
<label>
<input type="radio" name="config"><img
src="images/config_S.png" width="38px" height="38px"/>
</label>
<label>
<input type="radio" name="config" checked="checked"><img src="images/config_L.png"
width="38px"
height="38px"/>
</label>
<br>DyNCA Config
</div>
<div id='alignSelect' class="button-group">
<label>
<input type="radio" checked="checked" name="align"><img
src="images/icons.svg#alignRegular"/>
</label>
<label>
<input type="radio" name="align"><img src="images/icons.svg#alignPolar"/>
</label>
<label>
<input type="radio" name="align"><img src="images/icons.svg#alignDipole"/>
</label>
<br>Local Coordinate <br>Transformation
</div>
<div>
<input type="range" id="resolution" min="-2" max="2" step="1" value="0"></br>Resolution:
<label
id="resolutionLabel"></label>
</div>
<!-- <div id='RotPosEmb' class="button-group">-->
<!-- <label>-->
<!-- <input type="radio" checked="checked" name="rpe"><img src="images/icons.svg#gridSquare"/>-->
<!-- </label>-->
<!-- <label>-->
<!-- <input type="radio" name="grid" id='nrpe'><img src="images/icons.svg#gridHex"/>-->
<!-- </label>-->
<!-- <br>Rotate Positional <br>Embedding-->
<!-- </div>-->
</div>
<br>
<div id="demo-tip">
<img id="pointer" src="images/pointer.svg">
<div class="hint ">
Click or tap the image to erase parts of the pattern.
</div>
</div>
</div>
<div id="pattern-controls">
<div class="pattern-selector-title" id="pattern_selector_title">
<span>Target Appearance</span>
</div>
<!-- <div class="texture-overlay overlaygrad">-->
<!-- </div>-->
<!-- <div class="texture-overlay overlayicon">-->
<!-- </div>-->
<div id="texture" class="pattern-selector">
<!-- a hacky way to add some padding, so the fadeout doesn't cover items in the scroll. -->
<div class="whitespace"></div>
</div>
<div class="pattern-selector-title" id="motion_selector_title">
<span>Target Vector Field</span>
</div>
<!-- <div class="motion-overlay overlaygrad" id="motion_overlay_grad">-->
<!-- </div>-->
<!-- <div class="motion-overlay overlayicon" id="motion_overlay_icon">-->
<!-- </div>-->
<div id="motion" class="pattern-selector">
<div class="whitespace"></div>
</div>
<div id="texhint">
<div id="origtex">
</div>
<span id="texhinttext"></span>
<div id="origmot"></div>
<span id="mothinttext"></span>
</div>
</div>
</div>
</div>
</div>
</section>
<section class="section hero is-light">
<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>
Current Dynamic Texture Synthesis (DyTS) models can synthesize realistic videos. However, they
require a slow iterative optimization process to synthesize a single fixed-size short video, and
they do not offer any post-training control over the synthesis process. We propose Dynamic
Neural Cellular Automata (DyNCA), a framework for real-time and controllable dynamic texture
synthesis. Our method is built upon the recently introduced NCA models and can synthesize
infinitely long and arbitrary-sized realistic video textures in real time. We quantitatively and
qualitatively evaluate our model and show that our synthesized videos appear more realistic than
the existing results. We improve the SOTA DyTS performance by 2∼4 orders of magnitude. Moreover,
our model offers several real-time video controls including motion speed, motion direction, and
an editing brush tool. We exhibit our trained models in an online interactive demo that runs on
local hardware and is accessible on personal computers and smartphones.
</p>
</div>
</div>
</div>
<!--/ Abstract. -->
<!-- Paper video. -->
<!-- <div class="columns is-centered has-text-centered">-->
<!-- <div class="column is-four-fifths">-->
<!-- <h2 class="title is-3">Video</h2>-->
<!-- <div class="publication-video">-->
<!-- <iframe src="https://www.youtube.com/embed/MrKrnHhk8IA?rel=0&showinfo=0"-->
<!-- frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>-->
<!-- </div>-->
<!-- </div>-->
<!-- </div>-->
<!--/ Paper video. -->
</div>
</section>
<section class="section is-light">
<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">How it works?</h2>
<div class="content has-text-justified">
<img src="images/dynca_video_cut.png" height="100%"/>
<br><br>
<p>
Starting from a seed state, DyNCA iteratively updates it, generating an image sequence. We
extract images from this sequence and compare them with an appearance target as well as a motion
target to obtain the DyNCA training objectives. After training, DyNCA can adapt to seeds of
different height and width, and synthesize videos with arbitrary length. Sequentially applying
DyNCA updates on the seed synthesizes dynamic texture videos in real-time.
</p>
<br>
<p> The figure below illustrates a single <i>DyNCA</i> step.</p>
<img src="images/NCA_Step_cut.png" height="100%"/>
<br><br>
<p>
Given an input state $\mathbf{S}^t \in R^{H \times W \times C}$ at time step $t$,
each cell perceives its neighbors on various scales with the same perception layer.
The perception tensor of each scale is then upsampled and summed up, and is concatenated with
the positional encoding tensor $\mathbf{P}_{xy}$.
Each cell $s^t_{ij}$ then applies the same update rule, parametrized by a small neural network.
Finally, all cells perform stochastic residual update to determine the state of the cells in
time $t + \Delta t$.
</p>
</div>
</div>
</div>
</div>
</section>
<section class="section hero is-light" id="style_transfer">
<div class="container is-max-desktop">
<h2 class="title is-3 has-text-centered">Dynamic Style Transfer</h2>
<div class="columns is-centered">
<!-- Visual Effects. -->
<div class="column">
<div class="content">
<h3 class="title is-5 has-text-centered">Target Appearance</h3>
<p>
Our <i>DyNCA</i> model learns a desired texture pattern from a given target
appearance image. <br><br><br><br>
</p>
<img src="images/style_transfer_appearance/cartoon_fire_6.png" height="100%"/>
<img src="images/style_transfer_appearance/cartoon_water_1.png" height="100%"/>
<img src="images/style_transfer_appearance/USA1.png" height="100%"/>
<img src="images/style_transfer_appearance/cartoon_fire_4.png" height="100%"/>
<img src="images/style_transfer_appearance/cartoon_water_3.png" height="100%"/>
<img src="images/style_transfer_appearance/LGBT.jpg" height="100%"/>
<img src="images/style_transfer_appearance/cartoon_fire_2.png" height="100%"/>
<img src="images/style_transfer_appearance/cartoon_water_2.png" height="100%"/>
<img src="images/style_transfer_appearance/cartoon_fire_1.png" height="100%"/>
<img src="images/style_transfer_appearance/cartoon_water_4.png" height="100%"/>
<img src="images/style_transfer_appearance/cartoon_fire_3.png" height="100%"/>
</div>
</div>
<!--/ Visual Effects. -->
<!-- Matting. -->
<div class="column">
<h3 class="title is-5 has-text-centered">Target Dynamics</h3>
<div class="columns is-centered">
<div class="column content">
<p>
Our model can learn motion from a target video and transfer this motion to the target
appearance image. <br><br><br><br>
</p>
<video controls playsinline height="100%">
<source src="videos/real_mp4/flames.mp4" type="video/mp4">
</video>
<video controls playsinline height="100%">
<source src="videos/real_mp4/sea_2.mp4" type="video/mp4">
</video>
<video controls playsinline height="100%">
<source src="videos/real_mp4/flag_2.mp4" type="video/mp4">
</video>
<video controls playsinline height="100%">
<source src="videos/real_mp4/fireplace_2.mp4" type="video/mp4">
</video>
<video controls playsinline height="100%">
<source src="videos/real_mp4/sea_2.mp4" type="video/mp4">
</video>
<video controls playsinline height="100%">
<source src="videos/real_mp4/flag_2.mp4" type="video/mp4">
</video>
<video controls playsinline height="100%">
<source src="videos/real_mp4/fireplace_1.mp4" type="video/mp4">
</video>
<video controls playsinline height="100%">
<source src="videos/real_mp4/sea_2.mp4" type="video/mp4">
</video>
<video controls playsinline height="100%">
<source src="videos/real_mp4/flames.mp4" type="video/mp4">
</video>
<video controls playsinline height="100%">
<source src="videos/real_mp4/water_3.mp4" type="video/mp4">
</video>
<video controls playsinline height="100%">
<source src="videos/real_mp4/fireplace_2.mp4" type="video/mp4">
</video>
</div>
</div>
</div>
<div class="column">
<h3 class="title is-5 has-text-centered">Synthesized Results</h3>
<div class="columns is-centered">
<div class="column content">
<p>
Our model synthesizes realistic dynamic texture videos. Each synthesized video frame
resembles the target appearance, while the the concatenation of frames induces the motion of
the target dynamics.
</p>
<video controls playsinline height="100%">
<source src="videos/style_transfer/flames-cartoon_fire_6.mp4" type="video/mp4">
</video>
<video controls playsinline height="100%">
<source src="videos/style_transfer/sea_2-cartoon_water_1.mp4" type="video/mp4">
</video>
<video controls playsinline height="100%">
<source src="videos/style_transfer/flag_2-USA.mp4" type="video/mp4">
</video>
<video controls playsinline height="100%">
<source src="videos/style_transfer/fireplace_2-cartoon_fire_4.mp4" type="video/mp4">
</video>
<video controls playsinline height="100%">
<source src="videos/style_transfer/sea_2-cartoon_water_3.mp4" type="video/mp4">
</video>
<video controls playsinline height="100%">
<source src="videos/style_transfer/flag_2-LGBT.mp4" type="video/mp4">
</video>
<video controls playsinline height="100%">
<source src="videos/style_transfer/fireplace_1-cartoon_fire_2.mp4" type="video/mp4">
</video>
<video controls playsinline height="100%">
<source src="videos/style_transfer/sea_2-cartoon_water_2.mp4" type="video/mp4">
</video>
<video controls playsinline height="100%">
<source src="videos/style_transfer/flames-cartoon_fire_1.mp4" type="video/mp4">
</video>
<video controls playsinline height="100%">
<source src="videos/style_transfer/water_3-cartoon_water_4.mp4" type="video/mp4">
</video>
<video controls playsinline height="100%">
<source src="videos/style_transfer/fireplace_2-cartoon_fire_3.mp4" type="video/mp4">
</video>
</div>
</div>
</div>
</div>
</div>
</section>
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>@InProceedings{pajouheshgar2022dynca,
title = {DyNCA: Real-Time Dynamic Texture Synthesis Using Neural Cellular Automata},
author = {Pajouheshgar, Ehsan and Xu, Yitao and Zhang, Tong and S{\"u}sstrunk, Sabine},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2023},
}</code></pre>
</div>
</section>
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<p>
This website is licensed under a <a rel="license"
href="http://creativecommons.org/licenses/by-sa/4.0/">Creative
Commons Attribution-ShareAlike 4.0 International License</a>.
</p>
<p>
Our demo is built upon the source code of<a href="https://distill.pub/selforg/2021/textures/">
Self-Organizing Textures</a> paper on distill.
</p>
<p>
Website source code is based on the <a href="https://nerfies.github.io/"> Nerfies</a> project page.
If
you want to reuse their <a href="https://github.com/nerfies/nerfies.github.io">source code</a>,
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