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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="description"
content="DiffPoseTalk: Speech-Driven Stylistic 3D Facial Animation and Head Pose Generation via Diffusion Models.">
<meta name="keywords" content="3D facial animation, speech-driven animation, diffusion model">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>DiffPoseTalk: Speech-Driven Stylistic 3D Facial Animation and Head Pose Generation via Diffusion Models</title>
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More Research
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<a class="navbar-item" href="./">
DiffPoseTalk
</a>
<a class="navbar-item" href="https://raineggplant.github.io/FEE4TV/">
C^2 Facial Expression Editing in Talking Videos
</a>
</div>
</div>
</div>
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<section class="hero">
<div class="hero-body" style="padding-top: 2em;">
<div class="container is-max-desktop">
<div class="columns is-centered">
<div class="column has-text-centered">
<h1 class="title is-1 publication-title">DiffPoseTalk: Speech-Driven Stylistic 3D Facial Animation and Head
Pose
Generation via Diffusion Models</h1>
<div class="is-size-3 publication-venue">
SIGGRAPH 2024 (Journal)
</div>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://github.com/RainEggplant">Zhiyao Sun</a><sup>1</sup>,</span>
<span class="author-block">
Tian Lv<sup>1</sup>,</span>
<span class="author-block">
Sheng Ye<sup>1</sup>,
</span>
<span class="author-block">
Matthieu Lin<sup>1</sup>,
</span>
<span class="author-block">
Jenny Sheng<sup>1</sup>,
</span>
<span class="author-block">
Yu-Hui Wen<sup>2</sup>,
</span>
<span class="author-block">
Minjing Yu<sup>3</sup>,
</span>
<span class="author-block">
<a href="https://yongjinliu.github.io/">Yong-Jin Liu</a><sup>1</sup>
</span>
</div>
<div class="column has-text-centered">
<div class="publication-links">
<!-- PDF Link. -->
<span class="link-block">
<a href="https://arxiv.org/abs/2310.00434" 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>
<!-- Video Link. -->
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<span>Video</span>
</a>
</span>
<!-- Code Link. -->
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<a href="https://github.com/DiffPoseTalk/DiffPoseTalk"
class="external-link button is-normal is-rounded is-dark">
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<i class="fab fa-github"></i>
</span>
<span>Code</span>
</a>
</span>
<!-- Dataset Link. -->
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<a href="https://github.com/DiffPoseTalk/DiffPoseTalk/tree/main/datasets/HDTF_TFHP"
class="external-link button is-normal is-rounded is-dark">
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<span>Dataset</span>
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<span>Poster</span>
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<span>Data</span>
</a> -->
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</section>
<section class="hero is-light is-small teaser">
<div class="container is-max-desktop">
<div class="hero-body">
<img src="./static/images/teaser.jpg" alt="teaser">
<h2 class="subtitle has-text-centered">
<span class="dnerf">DiffPoseTalk</span> generates diverse and stylistic 3D facial motions with head poses from speech.
</h2>
</div>
</div>
</section>
<section class="section">
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<!-- Abstract. -->
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<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
The generation of stylistic 3D facial animations driven by speech poses a significant challenge as it
requires learning
a many-to-many mapping between speech, style, and the corresponding natural facial motion. However,
existing methods
either employ a deterministic model for speech-to-motion mapping or encode the style using a one-hot
encoding scheme.
Notably, the one-hot encoding approach fails to capture the complexity of the style and thus limits
generalization
ability. In this paper, we propose DiffPoseTalk, a generative framework based on the diffusion model
combined with a
style encoder that extracts style embeddings from short reference videos. During inference, we employ
classifier-free
guidance to guide the generation process based on the speech and style. We extend this to include the
generation of head
poses, thereby enhancing user perception. Additionally, we address the shortage of scanned 3D talking face
data by
training our model on reconstructed 3DMM parameters from a high-quality, in-the-wild audio-visual dataset.
Our extensive
experiments and user study demonstrate that our approach outperforms state-of-the-art methods. The code
and dataset are
at <a href="https://diffposetalk.github.io">https://diffposetalk.github.io</a>.
</p>
</div>
</div>
</div>
<!--/ Abstract. -->
<!-- Method. -->
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Method</h2>
<img src="./static/images/pipeline.jpg" alt="pipeline">
<h2 class="subtitle has-text-centered"><b>Left:</b> Transformer-based denoising network. <b>Right:</b> Speaking style encoder.</h2>
</div>
</div>
<!--/ Method. -->
</div>
</section>
<section class="hero is-light is-small">
<div class="container is-max-desktop" style="width: 100%;">
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<!-- Paper video. -->
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allow="autoplay; encrypted-media" allowfullscreen></iframe>
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</div>
</div>
<!--/ Paper video. -->
</div>
</div>
</section>
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>@article{sun2024diffposetalk,
title={DiffPoseTalk: Speech-Driven Stylistic 3D Facial Animation and Head Pose Generation via Diffusion Models},
author={Sun, Zhiyao and Lv, Tian and Ye, Sheng and Lin, Matthieu and Sheng, Jenny and Wen, Yu-Hui and Yu, Minjing and Liu, Yong-Jin},
journal={ACM Transactions on Graphics (TOG)},
doi={10.1145/3658221},
volume={43},
number={4},
articleno={46},
numpages={9},
year={2024},
publisher={ACM New York, NY, USA}
}</code></pre>
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