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        content="Identity-Preserving talking head generation with fast personalized adaptation.">
  <meta name="keywords" content="Talking Head, Meta Learning, Temporal-consistent Super-resolution  ">
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  <title>MetaPortrait: Identity-Preserving Talking Head Generation with Fast Personalized Adaptation</title>

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          <h1 class="title is-1 publication-title">MetaPortrait: Identity-Preserving Talking Head Generation with Fast Personalized Adaptation</h1>
          <div class="is-size-5 publication-authors">
            <span class="author-block">
              <a href="http://home.ustc.edu.cn/~zhangbowen">Bowen Zhang</a><sup>1*</sup>,</span>
            <span class="author-block">
              <a href="https://chenyangqiqi.github.io/">Chenyang Qi</a><sup>2*</sup>,</span>
            <span class="author-block">
              <a href="https://panzhang0212.github.io/">Pan Zhang</a><sup>1</sup>,
            </span>
            <span class="author-block">
              <a href="https://bo-zhang.me/">Bo Zhang</a><sup>3</sup>,
            </span>
            <br>
            <span class="author-block">
              <a href="https://dl.acm.org/profile/81487650131">HsiangTao Wu</a><sup>3</sup>,
            </span>
            <span class="author-block">
              <a href="http://www.dongchen.pro/">Dong Chen</a><sup>3</sup>,
            </span>
            <span class="author-block">
              <a href="https://cqf.io/">Qifeng Chen</a><sup>2</sup>,
            </span>
            <span class="author-block">
              <a href="http://en.auto.ustc.edu.cn/2021/0616/c26828a513186/page.htm">Yong Wang</a><sup>1</sup>,
            </span>
            <span class="author-block">
              <a href="https://www.microsoft.com/en-us/research/people/fangwen/">Fang Wen</a><sup>3</sup>
            </span>
          </div>

          <div class="is-size-5 publication-authors">
            <span class="author-block"><sup>1</sup>USTC,</span>
            <span class="author-block"><sup>2</sup>HKUST,</span>
            <span class="author-block"><sup>3</sup>Microsoft</span>
          </div>

          <div class="is-size-5 publication-venue">
            in CVPR 2023
          </div>

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                  <span>Paper</span>
                </a>
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                  <span>Code</span>
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  </div>
</section>

<section class="hero teaser">
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      <img src="static/images/Teaser.png" class="center">
      <h2 class="subtitle has-text-centered">
        Our MetaPortrait yields identity-preserving talking head generation.
      </h2>
    </div>
  </div>
</section>

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            <source src="./static/videos/Self_reconstruction_2.mp4"
                    type="video/mp4">
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            <source src="./static/videos/Fast_personalization_1.mp4"
                    type="video/mp4">
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          <video poster="" id="fullbody" autoplay controls muted loop playsinline height="100%">
            <source src="./static/videos/Fast_personalization_3.mp4"
                    type="video/mp4">
          </video>
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      </div>
    </div>
  </div>
</section>

<section class="section">
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        <h2 class="title is-3">Abstract</h2>
        <div class="content has-text-justified">
          <p>
            In this work, we propose an ID-preserving talking head generation framework, which advances previous methods in two aspects. First, as opposed to interpolating from sparse flow, we claim that dense landmarks are crucial to achieving accurate geometry-aware flow fields. Second, inspired by face-swapping methods, we adaptively fuse the source identity during synthesis, so that the network better preserves the key characteristics of the image portrait. Although the proposed model surpasses prior generation fidelity on established benchmarks, to further make the talking head generation qualified for real usage, personalized fine-tuning is usually needed. However, this process is rather computationally demanding that is unaffordable to standard users. To solve this, we propose a fast adaptation model using a meta-learning approach. The learned model can be adapted to a high-quality personalized model as fast as 30 seconds. Last but not the least, a spatial-temporal enhancement module is proposed to improve the fine details while ensuring temporal coherency. Extensive experiments prove the significant superiority of our approach over the state of the arts in both one-shot and personalized settings.
          </p>
        </div>
      </div>
    </div>
    <!--/ Abstract. -->

    <div class="columns is-centered has-text-centered">
      <div class="column is-four-fifths">
        <h2 class="title is-3">Method</h2>
        <div>
          <img src="static/images/Overall_framework.png" class="center">
        </div>
        <div class="content has-text-justified">
          <p>
            Given a source image and a driving video, we first extract their dense landmarks using a pretrained landmark detector. Then, we estimate warping flows between the source image and each driving frame according to concatenated input. We further refine the warped source input using an ID-preserving network. Finally, we enhance and upsample the 256x256 results to high-fidelity output in 512x512.
          </p>
        </div>

      </div>
    </div>
    <div class="columns is-centered has-text-centered">
      <div class="column is-four-fifths">
        <h2 class="title is-3">Video</h2>
        <h3 class="title is-4">Self Reconstruction</h3>
        <div class="publication-video">
          <iframe src="https://www.youtube.com/embed/9RHKK8JfQI4"
                  frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
        </div>
        <br>
        <div class="publication-video">
          <iframe src="https://www.youtube.com/embed/uPqrCLi-OS0"
                  frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
        </div>
        <br>
        <h3 class="title is-4">Cross Reenactment</h3>
        <div class="publication-video">
          <iframe src="https://www.youtube.com/embed/ccAReHsoEH0"
                  frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
        </div>
        <br>
        <div class="publication-video">
          <iframe src="https://www.youtube.com/embed/1KopIEOlYzk"
                  frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
        </div>
        <br>
        <div class="publication-video">
          <iframe src="https://www.youtube.com/embed/J_QexMlugOA"
                  frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
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        <br>
        <div class="publication-video">
          <iframe src="https://www.youtube.com/embed/cwlmz5d_R2A"
                  frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
        </div>
        <br>
        <h3 class="title is-4">Fast Personalization</h3>
        <div class="publication-video">
          <iframe src="https://www.youtube.com/embed/erCJQPEYPn8"
                  frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
        </div>
        <br>
        <div class="publication-video">
          <iframe src="https://www.youtube.com/embed/iKHKoWP_AoE"
                  frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
        </div>
        <br>
        <div class="publication-video">
          <iframe src="https://www.youtube.com/embed/bk9TWqrF1X4"
                  frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
        </div>
      </div>
    </div>
  </div>
</section>


<section class="section" id="BibTeX">
  <div class="container is-max-desktop content">
    <h2 class="title">BibTeX</h2>
    <pre><code>@article{zhang2022metaportrait,
        title={MetaPortrait: Identity-Preserving Talking Head Generation with Fast Personalized Adaptation},
        author={Bowen Zhang and Chenyang Qi and Pan Zhang and Bo Zhang and HsiangTao Wu and Dong Chen and Qifeng Chen and Yong Wang and Fang Wen},a
        journal={arXiv:2212.08062},
        year={2022},
  }</code></pre>
  </div>
</section>


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