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<!DOCTYPE html>
<html>
<head>
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<title>Mimic: Speaking Style Disentanglement for Speech-Driven 3D Facial Animation</title>
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<h1 class="title is-1 publication-title">Mimic: Speaking Style Disentanglement for Speech-Driven 3D Facial Animation</h1>
<div class="is-size-5 publication-authors">
<!-- Paper authors -->
<span class="author-block">
Hui Fu<sup>1,3</sup>,</span>
<span class="author-block">
Zeqing Wang<sup>2</sup>,</span>
<span class="author-block">
Ke Gong<sup>3</sup>,</span>
<span class="author-block">
Keze Wang<sup>2</sup>,</span>
<span class="author-block">
Tianshui Chen<sup>4</sup>,</span>
<span class="author-block">
Haojie Li<sup>3</sup>,</span>
<span class="author-block">
Haifeng Zeng<sup>3</sup>,</span>
<span class="author-block">
Wenxiong Kang<sup>1</sup></span>
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<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>South China University of Technology, <sup>2</sup>Sun Yat-sen University, <sup>3</sup>X-ERA.ai, <sup>4</sup>Guangdong University of Technology<br>AAAI 2024</span>
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<h2 class="subtitle has-text-centered">
To better evaluate the qualitative results produced by competitors (VOCA, MeshTalk, FaceFormer, CodeTalker, and Imitator) and our Mimic, we provide a supplemental video for demonstration and comparison.
</h2>
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<!-- Paper abstract -->
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<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
Speech-driven 3D facial animation aims to synthesize vivid facial animations that accurately synchronize with speech and match the unique speaking style. However, existing works primarily focus on achieving precise lip synchronization while neglecting to model the subject-specific speaking style, often resulting in unrealistic facial animations. To the best of our knowledge, this work makes the first attempt to explore the coupled information between the speaking style and the semantic content in facial motions. Specifically, we introduce an innovative speaking style disentanglement method, which enables arbitrary-subject speaking style encoding and leads to a more realistic synthesis of speech-driven facial animations. Subsequently, we propose a novel framework called Mimic to learn disentangled representations of the speaking style and content from facial motions by building two latent spaces for style and content, respectively. Moreover, to facilitate disentangled representation learning, we introduce four well-designed constraints: an auxiliary style classifier, an auxiliary inverse classifier, a content contrastive loss, and a pair of latent cycle losses, which can effectively contribute to the construction of the identity-related style space and semantic-related content space. Extensive qualitative and quantitative experiments conducted on three publicly available datasets demonstrate that our approach outperforms state-of-the-art methods and is capable of capturing diverse speaking styles for speech-driven 3D facial animation.
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<h2 class="subtitle has-text-centered">
Illustration of Mimic that learns two disentangled latent spaces for style and content, respectively.
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<h2 class="title">BibTeX</h2>
<pre><code>@inproceedings{hui2024Mimic,
title={Mimic: Speaking Style Disentanglement for Speech-Driven 3D Facial Animation},
author={Hui Fu, Zeqing Wang, Ke Gong, Keze Wang, Tianshui Chen, Haojie Li, Haifeng Zeng, Wenxiong Kang},
booktitle={The 38th Annual AAAI Conference on Artificial Intelligence (AAAI)},
year={2024}
}</code></pre>
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</section>
<!--End BibTex citation -->
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