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            <h3 class="xtitle is-1 publication-title">CustomListener:
              Text-guided Responsive Interaction for User-friendly Listening Head Generation</h3>
            <div class="is-size-5 publication-authors">
              <!-- Paper authors -->
              <span class="author-block"></span>
              <a target="_blank">Xi Liu</a><sup>*</sup>,
              </span>
              <span class="author-block"></span>
              <a target="_blank">Ying Guo</a><sup>*</sup>,
              </span>
              <span class="author-block"></span>
              <a target="_blank">Cheng Zhen</a>,
              </span>
              <span class="author-block"></span>
              <a target="_blank">Tong Li</a>,
              </span>
              <span class="author-block"></span>
              <a target="_blank">Yingying Ao</a>,
              </span>
              <span class="author-block"></span>
              <a target="_blank">Pengfei Yan</a>
              </span>
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            <div class="is-size-5 publication-authors">
              <span class="author-block">Vision AI Department, Meituan
                <br>CVPR 2024</span>
              <span class="eql-cntrb"><small><br><sup>*</sup>Indicates Equal Contribution</small></span>
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          <h2 class="title is-3">Abstract</h3>
            <div class="content has-text-justified">
              <p>
                Listening head generation aims to synthesize a non-verbal responsive listener head by modeling the
                correlation between the speaker and the listener in dynamic conversion.
                The applications of listener agent generation in virtual interaction have promoted many works achieving
                the diverse and fine-grained motion generation.
                However, they can only manipulate motions through simple emotional labels, but cannot freely control the
                listener's motions.
                Since listener agents should have human-like attributes (e.g. identity, personality) which can be freely
                customized by users, this limits their realism.
                In this paper, we propose a user-friendly framework called CustomListener to realize the free-form text
                prior guided listener generation. To achieve speaker-listener coordination, we design a Static to
                Dynamic Portrait module (SDP), which interacts with speaker information to transform static text into
                dynamic portrait token with completion rhythm and amplitude information.
                To achieve coherence between segments, we design a Past Guided Generation Module (PGG) to maintain the
                consistency of customized listener attributes through the motion prior, and utilize a diffusion-based
                structure conditioned on the portrait token and the motion prior to realize the controllable generation.
                To train and evaluate our model, we have constructed two text-annotated listening head datasets based on
                ViCo and RealTalk, which provide text-video paired labels. Extensive experiments have verified the
                effectiveness of our model.
              </p>
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              Coming soon.
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      <h2 class="title is-3">Overview</h2>
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        <p>
          Given user-customized texts, a speaker video, and a listener portrait image, our system can generate a
          listener video with natural facial motions (pose, expression).
        </p>
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        <h2 class="title is-3"><strong>Video1</strong>: Visual Results Conditioned on Different
          User-customized
          Attributes </h2>
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            <!-- <h3 class="subtitle has-text-centered"> -->
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            <video controls="" width="80%" height="80%">
              <!-- t=0.001 is a hack to make iPhone show video thumbnail -->
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        <h2 class="title is-3"><strong>Video2</strong>: Comparisons on the ViCo Dataset </h2>
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            <video controls="" width="80%" height="80%">
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        <h2 class="title is-3"><strong>Video3</strong>: Comparisons on the RealTalk Dataset </h2>
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            <video controls="" width="80%" height="80%">
              <!-- t=0.001 is a hack to make iPhone show video thumbnail -->
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        <h2 class="title is-3"><strong>Video4</strong>: Ablation on SDP module </h2>
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            <small>
              <!-- <h4 class="subtitle" style="text-align: left;"> -->
              <!-- <h6 class="subtitle"> -->
              <p style="text-align: left;">
                As shown in Video4, without SDP module, the model can only generate constant motions (always
                smile in this
                scenario). After incorporating SDP module, the generated listener motions can not only fluctuate
                with the
                speaker's semantics and movement amplitude (i.e. enhanced speaker-listener coordination), but
                also achieve
                progressive motion
                changes.
              </p>
              <!-- </h4> -->
            </small>
            <video controls="" width="80%" height="80%">
              <!-- t=0.001 is a hack to make iPhone show video thumbnail -->
              <!-- <source src="https://github.com/sadtalker/sadtalker.github.io/blob/main/static/videos/talking.mp4?raw=true" type="video/mp4"> -->
              <source src="static/videos/Ablation_on_SDP.mp4" type="video/mp4">
            </video>
          </div>
        </div>
      </div>
    </div>
  </section>

  <!-- Youtube video -->
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        <!-- Paper video. -->
        <h2 class="title is-3"><strong>Video5</strong>: Ablation on PGM module </h2>
        <div class="columns is-centered has-text-centered">
          <div class="column is-four-fifths">
            <!-- <h4 class="subtitle" style="text-align: left;"> -->
            <!-- <h6 class="subtitle"> -->
            <p style="text-align: left;">
              As shown in Video5, our proposed PGM module has two benefits: Firstly, with PGM module, the
              generated
              listener motions will be more coherent between adjacent segments. Furthermore, the PGM module
              can also
              help maintain past behavioral habits (e.g. frowning in the last sample of Video5).
              <!-- </h6> -->
            </p>
            <video controls="" width="80%" height="80%">
              <!-- t=0.001 is a hack to make iPhone show video thumbnail -->
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              <source src="static/videos/Ablation_on_PGM.mp4" type="video/mp4">
            </video>
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      </div>
    </div>
  </section>


  <!-- BibTex citation -->
  <!-- <section class="section" id="BibTeX">
    <div class="container is-max-desktop content">
      <h2 class="title">BibTeX</h2>
      <pre><code>@inproceedings{liu2024customlistener,
  title={Customlistener: Text-guided responsive interaction for user-friendly listening head generation},
  author={Liu, Xi and Guo, Ying and Zhen, Cheng and Li, Tong and Ao, Yingying and Yan, Pengfei},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={2415--2424},
  year={2024}
}</code></pre>
    </div>
  </section> -->
  <!--End BibTex citation -->


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