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<!DOCTYPE html>
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content="Face Adapter for Pre-Trained Diffusion Models with Fine-Grained ID and Attribute Control.">
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<h1 class="title is-1 publication-title">
<span style="background: linear-gradient(to right, indigo, skyblue, violet, indigo, violet); -webkit-background-clip: text; -webkit-text-fill-color: transparent;">
Face Adapter
</span>
<br> for Pre-Trained Diffusion Models <br> with Fine-Grained ID and Attribute Control
</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://scholar.google.com/citations?hl=zh-CN&user=08E500gAAAAJ">Yue Han</a><sup>1</sup>,</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=-OxQlHsAAAAJ&hl=zh-CN">Junwei Zhu</a><sup>2</sup>,</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=OMo43hMAAAAJ&hl=zh-CN">Keke He</a><sup>2</sup>,
</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?hl=zh-CN&user=1621dVIAAAAJ">Xu Chen</a><sup>2</sup>,
</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=h6tuBAcAAAAJ&hl=zh-CN">Yanhao Ge</a><sup>3</sup>,
</span>
<span class="author-block">
<a href="">Wei Li</a><sup>3</sup>,
</span>
<span class="author-block">
<a href="https://lxtgh.github.io/">Xiangtai Li</a><sup>4</sup>
</span>
<span class="author-block">
<a href="https://zhangzjn.github.io/">Jiangning Zhang</a><sup>2</sup>
</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=fqte5H4AAAAJ&hl=zh-CN">Chengjie Wang</a><sup>2</sup>
</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=qYcgBbEAAAAJ&hl=en">Yong Liu</a><sup>1</sup>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>Zhejiang University,</span>
<span class="author-block"><sup>2</sup>Tencent Youtu Lab,</span>
<span class="author-block"><sup>3</sup>VIVO,</span>
<span class="author-block"><sup>4</sup>Nanyang Technological University</span>
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<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
Previous techniques for face reenactment and swapping predominantly rely on GAN frameworks. However, recent research has shifted its focus towards leveraging pre-trained diffusion models for these tasks, owing to their superior generation capabilities. Nonetheless, training these models incurs significant computational costs, and the results have not yet attained satisfactory performance levels.
To address this issue, we introduce <i><b>Face-Adapter</b></i>, an efficient and effective adapter designed for high-precision and high-fidelity face editing by pre-trained diffusion models. <br>
</p>
<p>Our method contains: <br>
  ⭐ A <i><b><span style="color: YellowGreen"> Spatial Condition Generator</span> </b></i> that provides precise landmarks and background;<br>
  ⭐ A Plug-and-play <i><b><span style="color: Gold">Identity Encoder</span> </b></i> that transfers face embeddings to the text space;<br>
  ⭐ An <i><b><span style="color: PaleVioletRed">Attribute Controller</span> </b> </i> that integrates spatial conditions and detailed attributes. <br>
</p>
<p>
<i><b>Face-Adapter</b></i> achieves comparable or even superior performance in terms of motion control precision, ID retention capability, and generation quality compared to fully fine-tuned models in face reenactment/swapping tasks.
Additionally, <i><b>Face-Adapter</b></i> seamlessly integrates with popular pre-trained diffusion models such as various StableDiffusion models.
</p>
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<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
Previous techniques for face reenactment and swapping predominantly rely on GAN frameworks. However, recent research has shifted its focus towards leveraging pre-trained diffusion models for these tasks, owing to their superior generation capabilities. Nonetheless, training these models incurs significant computational costs, and the results have not yet attained satisfactory performance levels.
To address this issue, we introduce <i><b>Face-Adapter</b></i>, an efficient and effective adapter designed for high-precision and high-fidelity face editing by pre-trained diffusion models. <br>
</p>
<p>Our method contains: <br>
  ⭐ A <i><b><span style="color: YellowGreen"> Spatial Condition Generator</span> </b></i> that provides precise landmarks and background;<br>
  ⭐ A Plug-and-play <i><b><span style="color: Gold">Identity Encoder</span> </b></i> that transfers face embeddings to the text space;<br>
  ⭐ An <i><b><span style="color: PaleVioletRed">Attribute Controller</span> </b> </i> that integrates spatial conditions and detailed attributes. <br>
</p>
<p>
<i><b>Face-Adapter</b></i> achieves comparable or even superior performance in terms of motion control precision, ID retention capability, and generation quality compared to fully fine-tuned models in face reenactment/swapping tasks.
Additionally, <i><b>Face-Adapter</b></i> seamlessly integrates with popular pre-trained diffusion models such as various StableDiffusion models.
</p>
</div>
</div>
</div>
</div>
</section> -->
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<h2 class="title is-3" style="background: linear-gradient(to right, indigo, indigo, skyblue,indigo, indigo); -webkit-background-clip: text; -webkit-text-fill-color: transparent;"> Motivation</h3>
<h4 class="title is-5" style="text-align: center;">Face-Adapter aims to address the unsatisfactory performance of current SD adapters <br> in performing face reenactment/ swapping.</h4>
</div>
</div>
<div class="columns is-centered">
<!-- reenact. -->
<div class="column">
<div class="content">
<h4 class="title is-5" style="text-align: center;">⭐Face Reenactment</h4>
<p>
Current SD adapters for face editing struggle to follow <b>fine-grained target structure </b> with text-based attribute control.
</p>
<div class="item item-chair-tp">
<img src="./static/images/reenact.png"
class="interpolation-image"
alt="Interpolate start reference image."/>
</div>
</div>
</div>
<!--/ reenact -->
<!-- swap. -->
<div class="column">
<h4 class="title is-5" style="text-align: center;">⭐Face Swapping</h4>
<div class="columns is-centered">
<div class="column content">
<p>
<!-- <br> -->
Current SD adapters for face editing struggle to generate <b>facial detail</b> and handle <b>face shape changes</b> in face swapping.
</p>
<div class="item item-chair-tp">
<img src="./static/images/swap.png"
class="interpolation-image"
alt="Interpolate start reference image."/>
</div>
</div>
</div>
</div>
</div>
<!--/ swap. -->
<!-- Animation. -->
<!-- <div class="columns is-centered">
<div class="column is-full-width">
<h2 class="title is-3">Animation</h2>
<h3 class="title is-4">Interpolating states</h3>
<div class="content has-text-justified">
<p>
We can also animate the scene by interpolating the deformation latent codes of two input
frames. Use the slider here to linearly interpolate between the left frame and the right
frame.
</p>
</div>
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<div class="column is-3 has-text-centered">
<img src="./static/images/interpolate_start.jpg"
class="interpolation-image"
alt="Interpolate start reference image."/>
<p>Start Frame</p>
</div>
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<div id="interpolation-image-wrapper">
Loading...
</div>
<input class="slider is-fullwidth is-large is-info"
id="interpolation-slider"
step="1" min="0" max="100" value="0" type="range">
</div>
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<img src="./static/images/interpolate_end.jpg"
class="interpolation-image"
alt="Interpolation end reference image."/>
<p class="is-bold">End Frame</p>
</div>
</div>
<br/> -->
<!--/ Interpolating. -->
<!-- Re-rendering. -->
<div class="columns is-centered has-text-centered">
<div class="column">
<h2 class="title is-3" style="background: linear-gradient(to right, indigo, indigo, skyblue, indigo, indigo); -webkit-background-clip: text; -webkit-text-fill-color: transparent;"> Adapter Design</h3>
</div>
</div>
<!-- <h3 class="title is-4" style="text-align:center;">Adapter Design</h3> -->
<div class="content has-text-justified">
<p>
            ⭐ Fully disentangled ID, target structure and attribute control enable 'one-model-two-tasks'.<br>
            ⭐ Addressing overlooked issues.<br>
            ⭐ Simple yet effective, plug and play.<br>
</p>
</div>
<div class="content has-text-centered">
<video id="replay-video"
controls
muted
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playsinline
width="100%">
<source src="./static/videos/adapterdesign_compress.mp4"
type="video/mp4">
</video>
</div>
<!--/ Re-rendering. -->
<div class="columns is-centered has-text-centered">
<div class="column">
<h2 class="title is-3" style="background: linear-gradient(to right, indigo,indigo, skyblue,indigo, indigo); -webkit-background-clip: text; -webkit-text-fill-color: transparent;"> More Comparison Results</h3>
</div>
</div>
<!-- <h3 class="title is-4" style="text-align:center;">More Comparison Results</h3> -->
<div class="content has-text-justified">
            Beside SD Adapters, Face-Adapter sets itself apart from <br>
<p>
            ✨ <i><b><span style="color: Gold"> Diffusion-based </span> </b></i> reenactment/ swapping methods, <br>
                  by effectively using the <b>prior of SD</b> and <b>improving generation quality</b>. <br>
            ✨ <i><b><span style="color: Gold"> GAN-based </span> </b></i> reenactment/ swapping methods, <br>
                  by exceled in accommodating <b>large pose</b> and <b>face shape variations</b> <br>
                  and demonstrates exceptional performance in <b>background generation</b>. <br>
</p>
</div>
<div class="content has-text-centered">
</div>
<h4 class="title is-5" style="text-align: center;">⭐Face Reenactment</h4>
<div class="columns is-centered">
<div class="column content" style="text-align: center;">
<!-- <p> -->
<!-- <br> -->
<!-- Current SD adapters for face editing struggle to generate <b>facial detail</b> and handle <b>face shape changes</b> in face swapping.
</p> -->
<div class="item item-chair-tp">
<img src="./static/images/reenact3.png"
class="interpolation-image"
alt="Interpolate start reference image."/>
</div>
</div>
</div>
<h4 class="title is-5" style="text-align: center;">⭐Face Swapping</h4>
<div class="columns is-centered">
<div class="column content" style="text-align: center;">
<p>
<br>
=== Comparison Under Large Face Shape Variations ===
</p>
<div class="item item-chair-tp">
<img src="./static/images/swapshape.png"
class="interpolation-image"
alt="Interpolate start reference image."/>
</div>
</div>
</div>
<div class="columns is-centered">
<div class="column content" style="text-align: center;" >
<p>
<br>
=== Comparison Under Large Pose Variations ===
</p>
<div class="item item-chair-tp">
<img src="./static/images/swappose.png"
class="interpolation-image"
alt="Interpolate start reference image."/>
</div>
</div>
</div>
</div>
</div>
<!--/ Animation. -->
<!-- <section class="section">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
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<h2 class="title is-3">Abstract</h2>
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<p>
Previous techniques for face reenactment and swapping predominantly rely on GAN frameworks. However, recent research has shifted its focus towards leveraging pre-trained diffusion models for these tasks, owing to their superior generation capabilities. Nonetheless, training these models incurs significant computational costs, and the results have not yet attained satisfactory performance levels.
To address this issue, we introduce <i><b>Face-Adapter</b></i>, an efficient and effective adapter designed for high-precision and high-fidelity face editing by pre-trained diffusion models. <br>
</p>
<p>Our method contains: <br>
  ⭐ A <i><b><span style="color: YellowGreen"> Spatial Condition Generator</span> </b></i> that provides precise landmarks and background;<br>
  ⭐ A Plug-and-play <i><b><span style="color: Gold">Identity Encoder</span> </b></i> that transfers face embeddings to the text space;<br>
  ⭐ An <i><b><span style="color: PaleVioletRed">Attribute Controller</span> </b> </i> that integrates spatial conditions and detailed attributes. <br>
</p>
<p>
<i><b>Face-Adapter</b></i> achieves comparable or even superior performance in terms of motion control precision, ID retention capability, and generation quality compared to fully fine-tuned models in face reenactment/swapping tasks.
Additionally, <i><b>Face-Adapter</b></i> seamlessly integrates with popular pre-trained diffusion models such as various StableDiffusion models.
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There's a lot of excellent work that was introduced around the same time as ours.
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<p>
<a href="https://arxiv.org/abs/2104.09125">Progressive Encoding for Neural Optimization</a> introduces an idea similar to our windowed position encoding for coarse-to-fine optimization.
</p>
<p>
<a href="https://www.albertpumarola.com/research/D-NeRF/index.html">D-NeRF</a> and <a href="https://gvv.mpi-inf.mpg.de/projects/nonrigid_nerf/">NR-NeRF</a>
both use deformation fields to model non-rigid scenes.
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Some works model videos with a NeRF by directly modulating the density, such as <a href="https://video-nerf.github.io/">Video-NeRF</a>, <a href="https://www.cs.cornell.edu/~zl548/NSFF/">NSFF</a>, and <a href="https://neural-3d-video.github.io/">DyNeRF</a>
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<p>
There are probably many more by the time you are reading this. Check out <a href="https://dellaert.github.io/NeRF/">Frank Dellart's survey on recent NeRF papers</a>, and <a href="https://github.com/yenchenlin/awesome-NeRF">Yen-Chen Lin's curated list of NeRF papers</a>.
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Check out our paper and mess around with our code! <br>
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<pre><code>@article{park2021nerfies,
author = {Park, Keunhong and Sinha, Utkarsh and Barron, Jonathan T. and Bouaziz, Sofien and Goldman, Dan B and Seitz, Steven M. and Martin-Brualla, Ricardo},
title = {Nerfies: Deformable Neural Radiance Fields},
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