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<h1 class="title is-1 publication-title">DiffMotion: Speech-Driven Gesture Synthesis Using Denoising Diffusion Model</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://paperswithcode.com/search?q=author%3AFan+Zhang">Fan Zhang</a><sup>1</sup><sup>,2</sup>,</span>
<span class="author-block">
<a href="https://paperswithcode.com/search?q=author%3ANaye+Ji">Naye Ji</a><sup>2</sup>,</span>
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<a href="https://paperswithcode.com/search?q=author%3AFuxing+Gao">Fuxing Gao</a><sup>2</sup>,
</span>
<span class="author-block">
<a href="https://paperswithcode.com/search?q=author%3AYongping+Li">Yongping Li </a><sup>1</sup><sup>,3</sup>,
</span>
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<span class="author-block"><sup>1</sup>Macau University of Science and Technology,</span>
<span class="author-block"><sup>2</sup>Communication University of Zhejiang</span>
<span class="author-block"><sup>3</sup>, Ningbo University of Finance & Economic</span>
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This paper has been accepted by 29th International Conference on MultiMedia Modeling on 15th Sept 2022.
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<h2 class="title is-3">Abstract</h2>
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<p>
Speech-driven gesture synthesis is a field of growing interest in virtual human creation.
However, a critical challenge is the inherent intricate one-to-many mapping between speech and gestures.
Previous studies have explored and achieved significant progress with generative models.
Notwithstanding, most synthetic gestures are still vastly less natural.
</p>
<p>
This paper presents DiffMotion, a novel speech-driven gesture synthesis architecture based on diffusion models.
The model comprises an autoregressive temporal encoder and a denoising diffusion probability Module.
The encoder extracts the temporal context of the speech input and historical gestures.
The diffusion module learns a parameterized Markov chain to gradually convert a simple distribution into a complex distribution and generates the gestures according to the accompanied speech.
Compared with baselines, objective and subjective evaluations confirm that our approach can produce natural and diverse gesticulation and demonstrate the benefits of diffusion-based models on speech-driven gesture synthesis.
</p>
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<!-- <p>Random samples from DiffMotion can give many distinct yet natural output-->
<!-- gestures within and between sequences, even if the input speech audio is the same.</p>-->
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<!-- <p> ℰ_θ contains the similar architecture employed in Rasul, K., Seward, C., Schuster, I., Vollgraf, R.: Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting.</p>-->
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<h2 class="title">BibTeX</h2>
<pre><code>@inproceedings{zhang2023diffmotion,
title={DiffMotion: Speech-Driven Gesture Synthesis Using Denoising Diffusion Model},
author={Zhang, Fan and Ji, Naye and Gao, Fuxing and Li, Yongping},
booktitle={MultiMedia Modeling: 29th International Conference, MMM 2023, Bergen, Norway, January 9--12, 2023, Proceedings, Part I},
pages={231--242},
year={2023},
organization={Springer}
}</code></pre>
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