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<a role="button" class="navbar-burger" aria-label="menu" aria-expanded="false">
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<span>Organization</span>
</a>
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<a class="navbar-link">
More Work
</a>
<div class="navbar-dropdown">
<a class="navbar-item" href="https://stablesigner.github.io/">
Stable Signer
</a>
<a class="navbar-item" href="https://signerx.github.io/SignX/">
SignX
</a>
<a class="navbar-item" href="https://signllm.github.io/">
SignLLM
</a>
<a class="navbar-item" href="https://signdiff.github.io">
SignDiff
</a>
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<section class="hero">
<div class="hero-body">
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<h2 class="title is-2 publication-title">SignDiff: Diffusion Model for American Sign Language Production</h2>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://x.com/SenFang01">Sen Fang</a><sup>1</sup>,</span>
<span class="author-block">
<a>Chunyu Sui</a><sup>2*</sup>,</span>
<span class="author-block">
<a>Yanghao Zhou</a><sup>3</sup>,
</span>
<span class="author-block">
<a>Xuedong Zhang</a><sup>4</sup>,
</span>
<span class="author-block">
<a>Hongbin Zhong</a><sup>5</sup>,
</span><br>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=lxCqdpoAAAAJ&hl=en">Yapeng Tian</a><sup>6</sup>
</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?hl=en&user=TuEwcZ0AAAAJ">Chen Chen</a><sup>7</sup>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>Rutgers University,</span>
<span class="author-block"><sup>2</sup>Columbia University</span>
<span class="author-block"><sup>3</sup>National University of Singapore,</span>
<span class="author-block"><sup>4</sup>Victoria University</span>
<span class="author-block"><sup>5</sup>Georgia Institute of Technology,</span>
<span class="author-block"><sup>6</sup>The University of Texas at Dallas</span>
<span class="author-block"><sup>7</sup>University of Central Florida</span>
</div>
<div class="column has-text-centered">
<div class="publication-links">
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<a href="https://arxiv.org/abs/2308.16082" class="external-link button is-normal is-rounded is-dark">
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<span>Paper</span>
</a>
</span>
<!-- Dataset Link. -->
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<a href="https://github.com/SignDiff/Processed-Data" class="external-link button is-normal is-rounded is-dark">
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<span>Data</span>
</a>
</span></div>
</div>
</div>
</div>
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</div>
</section>
<section class="hero teaser">
<div class="container is-max-desktop">
<div class="hero-body">
<div class="image-container">
<img id="teaser" src="SignDiff_files/cover.png" alt="封面图片">
</div>
<h2 class="subtitle has-text-centered">
<span class="dnerf">SignDiff</span> can convert images/videos of skeletal poses into more realistic images/videos.
</h2>
</div>
<p>* Co-author; The model will be commercialized, and the code will be a company asset.</p>
</div>
</section>
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<!-- Abstract. -->
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<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
In this paper, we propose a dual-condition diffusion pre-training model named SignDiff that can generate human sign language speakers from a skeleton pose. SignDiff has a novel Frame Reinforcement Network called FR-Net, similar to dense human pose estimation work, which enhances the correspondence between text lexical symbols and sign language dense pose frames, reduces the occurrence of multiple fingers in the diffusion model.
</p>
<p>
In addition, we propose a new method for American Sign Language Production (ASLP), which can generate ASL skeletal pose videos from text input, integrating two new improved modules and a new loss function to improve the accuracy and quality of sign language skeletal posture and enhance the ability of the model to train on large-scale data.
</p>
<p>
We propose the first baseline for ASL production and report the scores of 17.19 and 12.85 on BLEU-4 on the How2Sign dev/test sets. We evaluated our model on the previous mainstream dataset PHOENIX14T, and the experiments achieved the SOTA results. In addition, our image quality far exceeds all previous results by 10 percentage points in terms of SSIM.
</p>
</div>
</div>
</div>
<!--/ Abstract. -->
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<h2 class="title is-3">Demo Video</h2>
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<div style="flex: 1; min-width: 30%;">
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</div>
<p>Final Videos vs. Ground Truth.</p>
</div>
</div>
<!-- Paper video. -->
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<div class="column is-four-fifths">
<h2 class="title is-3">Working Principle</h2>
<div class="publication-video">
<iframe src="./SignDiff_files/video/workflow_demo3.mp4" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen=""></iframe>
</div>
<p>Simple demonstration of workflow.</p>
</div>
</div>
<!--/ Paper video. -->
</div>
</section>
<!-- Previous Works Section -->
<section class="section">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Previous Works</h2>
<div class="columns is-centered">
<!-- SignSynth -->
<div class="column">
<div class="content">
<h2 class="title is-6">SignSynth</h2>
<p>
Pose2Video model driven by point-like conditions for posture data, based GAN.
</p>
<video width="300" controls autoplay loop muted playsinline src="./SignDiff_files/video/SignSynth1.mp4" frameborder="0"></video>
<video width="300" controls autoplay loop muted playsinline src="./SignDiff_files/video/SignSynth2.mp4" frameborder="0"></video>
</div>
</div>
<!-- Text2Sign -->
<div class="column">
<div class="content">
<h2 class="title is-6">Text2Sign</h2>
<p>
Image generation for skeletal posture control, which is based on pix2pixHD implementation.
</p>
<video width="300" controls autoplay loop muted playsinline src="./SignDiff_files/video/text2sign1.mp4" frameborder="0"></video>
<video width="300" controls autoplay loop muted playsinline src="./SignDiff_files/video/text2sign2.mp4" frameborder="0"></video>
</div>
</div>
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<h2 class="title is-3">Related Works</h2>
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<a href="https://github.com/BenSaunders27/ProgressiveTransformersSLP">https://github.com/BenSaunders27/ProgressiveTransformersSLP</a>
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<a href="https://github.com/lllyasviel/ControlNet">https://github.com/lllyasviel/ControlNet</a>
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<pre><code>@misc{fang2025signdiffdiffusionmodelamerican,
title={SignDiff: Diffusion Model for American Sign Language Production},
author={Sen Fang and Chunyu Sui and Yanghao Zhou and Xuedong Zhang and Hongbin Zhong and Yapeng Tian and Chen Chen},
year={2025},
eprint={2308.16082},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2308.16082},
}
@misc{fang2024signllm,
title={SignLLM: Sign Languages Production Large Language Models},
author={Sen Fang and Lei Wang and Ce Zheng and Yapeng Tian and Chen Chen},
year={2024},
eprint={2405.10718},
archivePrefix={arXiv},
primaryClass={cs.CV}
}</code></pre>
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