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<h1 class="title is-1 publication-title">PHOTOSWAP: Personalized Subject Swapping in Images</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://g-jing.github.io">Jing Gu</a><sup>1</sup>,</span>
<span class="author-block">
<a href="https://yilinwang.org/">Yilin Wang</a><sup>2</sup>,</span>
<span class="author-block">
<a href="http://nxzhao.com/">Nanxuan Zhao</a><sup>2</sup>,</span>
<span class="author-block">
<a href="https://tsujuifu.github.io/">Tsu-Jui Fu</a><sup>3</sup>,</span>
<span class="author-block">
<a href="https://wxiong.me/">Wei Xiong</a><sup>2</sup>,</span>
<span class="author-block">
<a href="https://qliu24.github.io/">Qing Liu</a><sup>2</sup>,</span>
<span class="author-block">
<a href="https://zzutk.github.io/">Zhifei Zhang</a><sup>2</sup>,</span>
<span class="author-block">
<a href="https://sites.google.com/site/hezhangsprinter/">He Zhang</a><sup>2</sup>,</span>
<span class="author-block">
<a href="https://cs-people.bu.edu/jmzhang/">Jianming Zhang</a><sup>2</sup>,</span>
<span class="author-block">
<a href="https://polaris79.wixsite.com/hjung">HyunJoon Jung</a><sup>2</sup>,</span>
<span class="author-block">
<a href="https://eric-xw.github.io/">Xin Eric Wang</a><sup>1</sup>,</span>
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<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>University of California, Santa Cruz,</span>
<span class="author-block"><sup>2</sup>Adobe,</span>
<span class="author-block"><sup>1</sup>University of California, Santa Barbara</span>
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<h2 class="subtitle has-text-centered">
<span class="dnerf">Nerfies</span> turns selfie videos from your phone into
free-viewpoint
portraits.
</h2>
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<h2 class="title is-3">Abstract</h2>
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<p>
In an era where images and visual content dominate our digital landscape, the ability to manipulate and
personalize these images has become a necessity.
Envision seamlessly substituting a tabby cat lounging on a sunlit window sill in a photograph with your
own playful puppy, all while preserving the original charm and composition of the image.
We present \emph{Photoswap}, a novel approach that enables this immersive image editing experience
through personalized subject swapping in existing images.
\emph{Photoswap} first learns the visual concept of the subject from reference images and then swaps it
into the target image using pre-trained diffusion models in a training-free manner. We establish that a
well-conceptualized visual subject can be seamlessly transferred to any image with appropriate self-attention
and cross-attention manipulation, maintaining the pose of the swapped subject and the overall coherence of the image.
Comprehensive experiments underscore the efficacy and controllability of \emph{Photoswap} in personalized
subject swapping. Furthermore, \emph{Photoswap} significantly outperforms baseline methods in human ratings across
subject swapping, background preservation, and overall quality, revealing its vast application potential, from
entertainment to professional editing.
</p>
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<h2 class="title is-3">Visual Effects</h2>
<p>
Using <i>nerfies</i> you can create fun visual effects. This Dolly zoom effect
would be impossible without nerfies since it would require going through a wall.
</p>
<video id="dollyzoom" autoplay controls muted loop playsinline height="100%">
<source src="./static/videos/dollyzoom-stacked.mp4"
type="video/mp4">
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<p>
As a byproduct of our method, we can also solve the matting problem by ignoring
samples that fall outside of a bounding box during rendering.
</p>
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<h3 class="title is-4">Interpolating states</h3>
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<p>
Photoswap can effortlessly replace the subject in a source image, which could be either synthetic (first two rows) or real
(bottom row), with a personalized subject specified in reference images, while preserving the original
subject pose and the composition of the source image.
</p>
</div>
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<div class="column is-3 has-text-centered">
<img src="./static/images/teaser-arxiv.png"
class="interpolation-image"
alt="Interpolate start reference image."/>
<p>Start Frame</p>
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class="interpolation-image"
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<p class="is-bold">End Frame</p>
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<br/>
<!--/ Interpolating. -->
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<!-- <h3 class="title is-4">Re-rendering the input video</h3>
<div class="content has-text-justified">
<p>
Using <span class="dnerf">Nerfies</span>, you can re-render a video from a novel
viewpoint such as a stabilized camera by playing back the training deformations.
</p>
</div>
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<video id="replay-video"
controls
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width="75%">
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<!--/ Animation. -->
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<h2 class="title is-3">Related Links</h2>
<div class="content has-text-justified">
<p>
There's a lot of excellent work that was introduced around the same time as ours.
</p>
<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.
</p>
<p>
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>
</p>
<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>.
</p>
</div>
</div>
</div> -->
<!--/ Concurrent Work. -->
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</section>
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<h2 class="title">BibTeX</h2>
<pre><code>@article{gu2023photoswap,
author = {Gu, Jing and Wang, Yilin, and Zhao, Nanxuan and Fu, Tsu-Jui and Wei, Xiong and Liu, Qing and Zhang, Zhifei and Zhang, He and Zhang, Jianming and Jung, Hyunjoon and Wang, Eric Xing},
title = {PHOTOSWAP: Personalized Subject Swapping in Images},
journal = {arXiv},
year = {2023},
}</code></pre>
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