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  <meta name="description" content="Diverse Signer Avatars with Manual and Non-Manual Feature Modelling for Sign Language Production">
  <meta name="keywords" content="Gaussian Avatars, Monocular Avatar Reconstruction, Multi-view Diffusion">
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  <title>Diverse Signer Avatars with Manual and Non-Manual Feature Modelling for Sign Language Production</title>

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          <h1 class="title is-1 publication-title">Diverse Signer Avatars with Manual and Non-Manual Feature Modelling for
Sign Language Production</h1>
          <div class="is-size-5 publication-authors">
            <span class="author-block">
              <a href="https://mlakhal.github.io">Mohamed Ilyes Lakhal</a><sup>1</sup>,</span>
            <span class="author-block">
                <a href="https://scholar.google.com/citations?user=mvvgDvcAAAAJ&hl=en">Richard Bowden</a><sup>1</sup>,</span>
          </div>

          <div class="is-size-5 publication-authors">
            <span class="author-block"><sup>1</sup>CVSSP, University of Surrey</span>
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<section class="hero teaser">
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      <img src="teaser_model.png" alt="Teaser Image" style="width: 100%; height: auto;">
      <h2 class="subtitle has-text-centered">
        Our goal is to generate photorealistic digital avatars that preserve essential sign language cues — such as facial expressions, hand movements, and mouthing — while enabling diversity across ethnicities and adaptability to different sign languages.
      </h2>
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</section>

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        <h2 class="title has-text-centered">Abstract</h2>
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          <p>
            The diversity of sign representation is essential for Sign Language Production (SLP) as it captures variations in appearance,
            facial expressions, and hand movements. However, existing SLP models are often unable to capture diversity while preserving
            visual quality and modelling non-manual attributes such as emotions. To address this problem, we propose a novel approach that
            leverages Latent Diffusion Model (LDM) to synthesise photorealistic digital avatars from a generated reference image.
            We propose a novel sign feature aggregation module that explicitly models the non-manual features (<i>e.g.</i>, the face) and
            the manual features (<i>e.g.</i>, the hands). We show that our proposed module ensures the preservation of linguistic content
            while seamlessly using reference images with different ethnic backgrounds to ensure diversity.
            Experiments on the YouTube-SL-25 sign language dataset show that our pipeline achieves superior visual quality compared
            to state-of-the-art methods, with significant improvements on perceptual metrics.
          </p>
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    <!--/ Abstract -->
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</section>

<section class="section">
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      <h2 class="title has-text-centered">Method Overview</h2>
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        <p>
          Given a sequence of video frames \(\mathcal{V} = \{ \mathbf{V}_i \}\) of a sign language, our goal is to synthesise a diverse sequence \(\mathcal{O} = \{ \mathbf{O}_i \}\) that faithfully preserves both the manual and non-manual linguistic features while allowing for variation across different signer appearances.
          Our novel feature aggregation module, \(\Psi_{\text{motion}}\), uses multi-scale dilated convolutions with dilation rates \(d \in \{1, 2, 4\}\) to fuse fine-grained non-manual details (e.g., facial expressions) and coarse manual gestures (e.g., hand movements) into a unified representation.
          The LDM then generates each frame \(\mathbf{O}_i\) through an iterative denoising process in the latent space, guided by the aggregated features of \(\Psi_{\text{motion}}\), enabling the synthesis of signers with diverse ethnic and visual characteristics.
        </p>
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  <!--/ Method Overview -->
</section>

<section class="section">
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    <h2 class="title has-text-centered" style="margin-top: 2.5rem;">
      Comparisons against Baselines (DSGS)
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              <source src="./static/videos/DSGS/3002_01.mp4" type="video/mp4">
            </video>
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            <video poster="" autoplay="" muted loop="" style="pointer-events: none; width:100%;">
              <source src="./static/videos/DSGS/3013_01.mp4" type="video/mp4">
            </video>
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            <video poster="" autoplay="" muted loop="" style="pointer-events: none; width:100%;">
              <source src="./static/videos/DSGS/3054_01.mp4" type="video/mp4">
            </video>
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            <video poster="" autoplay="" muted loop="" style="pointer-events: none; width:100%;">
              <source src="./static/videos/DSGS/3073_01.mp4" type="video/mp4">
            </video>
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            <video poster="" autoplay="" muted loop="" style="pointer-events: none; width:100%;">
              <source src="./static/videos/DSGS/4332_01.mp4" type="video/mp4">
            </video>
          </div>
          <div class="item-6">
            <video poster="" autoplay="" muted loop="" style="pointer-events: none; width:100%;">
              <source src="./static/videos/DSGS/4348_01.mp4" type="video/mp4">
            </video>
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        <p>

        </p>
      </div>
    </div>


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    <h2 class="title has-text-centered" style="margin-top: 1.5rem;">
      Comparisons against Baselines (BSL)
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              <source src="./static/videos/BSL/HALFWAY.mp4" type="video/mp4">
            </video>
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          <div class="item-2">
            <video poster="" autoplay="" muted loop="" style="pointer-events: none; width:100%;">
              <source src="./static/videos/BSL/LEFT-OVER.mp4" type="video/mp4">
            </video>
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            <video poster="" autoplay="" muted loop="" style="pointer-events: none; width:100%;">
              <source src="./static/videos/BSL/PROVIDE-ME.mp4" type="video/mp4">
            </video>
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            <video poster="" autoplay="" muted loop="" style="pointer-events: none; width:100%;">
              <source src="./static/videos/BSL/REVEAL.mp4" type="video/mp4">
            </video>
          </div>
          <div class="item-5">
            <video poster="" autoplay="" muted loop="" style="pointer-events: none; width:100%;">
              <source src="./static/videos/BSL/SUPERVISION.mp4" type="video/mp4">
            </video>
          </div>
        </div>
      </div>
      <div class="content has-text-justified">
        <p>

        </p>
      </div>
    </div>

    <h2 class="title has-text-centered" style="margin-top: 1.5rem;">
      Adaptablity to any sign language
    </h2>
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          <div class="item-1">
            <video poster="" autoplay muted loop playsinline style="pointer-events: none; width:100%;">
              <source src="./static/videos/more_videos/000000.mp4" type="video/mp4">
            </video>
          </div>
          <div class="item-2">
            <video poster="" autoplay muted loop playsinline style="pointer-events: none; width:100%;">
              <source src="./static/videos/more_videos/3013_02.mp4" type="video/mp4">
            </video>
          </div>
          <div class="item-3">
            <video poster="" autoplay muted loop playsinline style="pointer-events: none; width:100%;">
              <source src="./static/videos/more_videos/rachel_OPHTHAMOLOGIST.mp4" type="video/mp4">
            </video>
          </div>
        </div>
      </div>
      <div class="content has-text-justified">
        <p>
          <b>Our model can work with any sign language. The first two examples shown are from Swiss-German Sign Language (DSGS) and the last is British Sign Language
        (BSL).</b>
        </p>
      </div>
    </div>

    <h2 class="title has-text-centered" style="margin-top: 1.5rem;">
      Comparison to other LDM methods
    </h2>
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          <div class="item-1">
            <video poster="" autoplay muted loop playsinline style="pointer-events: none; width:100%;">
              <source src="./static/videos/BSL/ldm_comparison_LeftOver.mp4" type="video/mp4">
            </video>
          </div>
        </div>
      </div>
      <div class="content has-text-justified">
        <p>
          <b>Our model can handle better the driving pose for sign language sequences.</b> <i>Note:</i> We apply a mask to report the metrics in Tab. 4 to remove the influence of the background.
        </p>
      </div>
    </div>

    <h2 class="title has-text-centered" style="margin-top: 1.5rem;">
      Sample diversity
    </h2>
    <div style="overflow:hidden;">
      <div class="container">
        <img
          id="ablation"
          src="teaser-diversity.png"
          alt="Ablation Studies teaser diversity"
          height="100%"
          style="display: block; margin: 0 auto;"
        />
        <div class="content has-text-justified">
          <p>
            <b>Our model can easily adapt to diverse images within the same pose sequence.</b>
          </p>
        </div>
      </div>
    </div>

    <h2 class="title has-text-centered" style="margin-top: 1.5rem;">
      User study
    </h2>
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      <div class="container">
        <img src="user_study.png" alt="User Study Results" style="width:100%; height:auto;" />
        <div class="content has-text-justified">
          <p>
            We highlight the accuracy of the synthesised videos (left), the realism of the videos (centre) and user preference (right).
          </p>
        </div>
      </div>
    </div>

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<section class="section" id="BibTeX">
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    <h2 class="title">BibTeX</h2>
    <pre><code>@article{diverse_sign,
      title={Diverse Signer Avatars with Manual and Non-Manual Feature Modelling for Sign Language Production},
      author={Mohamed Ilyes Lakhal and Richard Bowden},
      booktitle={ArXiv},
      year={2026}
    }</code></pre>
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