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    content="3DXTalker: Unifying Identity, Lip Sync, Emotion, and Spatial Dynamics in Expressive 3D Talking Avatars">
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  <title>3DXTalker: Unifying Identity, Lip Sync, Emotion, and Spatial Dynamics in Expressive 3D Talking Avatars</title>

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            <h1 class="title is-1 publication-title">3DXTalker: Unifying Identity, Lip Sync, Emotion, and Spatial Dynamics in Expressive 3D Talking Avatars</h1>
            <div class="is-size-3 publication-venue">

            </div>
            <div class="is-size-5 publication-authors">
              <span class="author-block">
                Zhongju Wang<sup>1</sup>,</span>
              <span class="author-block">
                Zhenhong Sun<sup>2</sup>,</span>
              <span class="author-block">
                Beier Wang<sup>1</sup>,
              </span>
              <span class="author-block">
                Yifu Wang<sup>3</sup>,
              </span>
              <span class="author-block">
                Daoyi Dong<sup>4</sup>,
              </span>
              <span class="author-block">
                Huadong Mo<sup>1</sup>,
              </span>
              <span class="author-block">
                Hongdong Li<sup>2</sup>
              </span>
            </div>

            <div class="is-size-5 publication-authors">
              <span class="author-block"><sup>1</sup>University of New South Wales,</span>
              <span class="author-block"><sup>2</sup>Australian National University,</span>
              <span class="author-block"><sup>3</sup>Vertex Lab,</span>
              <span class="author-block"><sup>4</sup>University of Technology Sydney</span>
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            <span class="dnerf">3DXTalker</span> generates identity-consistent, expressive 3D talking avatars from a single reference image and speech audio, achieving accurate lip synchronization, expressive emotion control, and natural head-pose dynamics.
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              Audio-driven 3D talking avatar generation is increasingly important in virtual communication, digital humans, and interactive media, where avatars must preserve identity, synchronize lip motion with speech, express emotion, and exhibit lifelike spatial dynamics, collectively defining a broader objective of <i>expressivity</i>. However, achieving this remains challenging due to insufficient training data with limited subject identities, narrow audio representations, and restricted explicit controllability. In this paper, we propose 3DXTalker, an expressive 3D talking avatar through data-curated identity modeling, audio-rich representations, and spatial dynamics controllability. 3DXTalker enables scalable identity modeling via 2D-to-3D data curation pipeline and disentangled representations, alleviating data scarcity and improving identity generalization. Then, we introduce frame-wise amplitude and emotional cues beyond standard speech embeddings, ensuring superior lip synchronization and nuanced expression modulation. These cues are unified by a flow-matching-based transformer for coherent facial dynamics. Moreover, 3DXTalker also enables natural head-pose motion generation while supporting stylized control via prompt-based conditioning. Extensive experiments show that 3DXTalker integrates lip synchronization, emotional expression, and head-pose dynamics within a unified framework, achieves superior performance in 3D talking avatar generation.
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      <!--/ Emotion Control -->

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      <pre><code>@misc{wang20263dxtalkerunifyingidentitylip,
      title={3DXTalker: Unifying Identity, Lip Sync, Emotion, and Spatial Dynamics in Expressive 3D Talking Avatars},
      author={Zhongju Wang and Zhenhong Sun and Beier Wang and Yifu Wang and Daoyi Dong and Huadong Mo and Hongdong Li},
      year={2026},
      eprint={2602.10516},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2602.10516},
}</code></pre>
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