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<!DOCTYPE html>
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<title>SelfSwapper: Self-Supervised Face Swapping via Shape Agnostic Masked AutoEncoder</title>
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RobustSwap
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MagiCapture
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<h1 class="title is-1 publication-title">SelfSwapper: Self-Supervised Face Swapping via Shape Agnostic Masked AutoEncoder</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://leejesse.github.io">Jaeseong Lee</a><sup>*</sup>,</span>
<span class="author-block">
<a href="https://junhahyung.github.io/">Junha Hyung</a><sup>*</sup>,</span>
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<span class="author-block"><sup></sup>Sohyun Jung</span>,
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<a href="https://sites.google.com/site/jaegulchoo/">Jaegul Choo</a><sup></sup>
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<span class="author-block"><sup></sup>ECCV 2024</span>
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<h2 class="title is-3">Key Idea</h2>
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<strong>(A) Comparison with Prior Works:</strong> Prior methods balance conflicting losses: <strong>reconstruction</strong> and <strong>identity</strong>. Our approach uses a <strong>self-supervised</strong> method with a clear ground truth, leading to more stable training.
</p>
<p style="text-align: left;">
<strong>(B) Ours Base vs. Ours Full:</strong> We compare our base model with our enhanced method, which adds <strong>perforation confusion</strong> and <strong>random mesh scaling</strong>. Green masks show source 3DMM, red masks target 3DMM, and orange masks their intersection.
</p>
<p style="text-align: left;">
When the source face is larger, the base model cuts off the jaw, and when smaller, it fails to fill in gaps. <strong>Ours Full</strong> solves both, generating realistic results.
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<h2 class="subtitle has-text-centered">
left: source video, right: swapped video
</h2>
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<source src="./static/videos/tom2jim.mp4"
type="video/mp4">
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left: source video, right: swapped video
</h2>
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<h2 class="title is-3">Comparison with Target-Oriented Methods</h2>
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<h2 class="title is-3">Comparison with Source-Oriented Methods</h2>
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<h2 class="title is-3">You may find interesting ...</h2>
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<p>
We focus on our research field as portrait manipulation. So we guide you guys to the our sibling works in the field of portrait manipulation.
</p>
<p>
<a href="https://arxiv.org/abs/2104.09125">RobustSwap</a> introduces a StyleGAN2-based face-swapping method with minimizing source attribute leakage.
</p>
<p>
<a href="https://www.albertpumarola.com/research/D-NeRF/index.html">MagiCapture</a> introduces a diffusion-based portrait personalization method with a novel training scheme.
</p>
</div>
</div>
</div>
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</section>
<section class="section" id="BibTeX">
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<h2 class="title">BibTeX</h2>
<pre><code>@article{lee2024selfswapper,
title={SelfSwapper: Self-Supervised Face Swapping via Shape Agnostic Masked AutoEncoder},
author={Lee, Jaeseong and Hyung, Junha and Jeong, Sohyun and Choo, Jaegul},
journal={arXiv preprint arXiv:2402.07370},
year={2024}
}</code></pre>
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