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<meta name="keywords" content="FaceVid-1K, Dataset, Talking Head Video">
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<h1 class="title is-1 publication-title">FaceVid-1K: A Large-Scale High-Quality Multiracial Human Face Video Dataset</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://scholar.google.com/citations?hl=zh-CN&user=L8tcNioAAAAJ">Donglin Di</a><sup>1</sup>,</span>
<span class="author-block">
<a href="https://github.com/fenghe12">He Feng</a><sup>1,2</sup>,</span>
<span class="author-block">
<a href="https://scholar.google.com.hk/citations?user=3-9aEOQAAAAJ&hl=zh-CN&oi=ao">Wenzhang Sun</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://scholar.google.com.hk/citations?user=BszRJxkAAAAJ&hl=zh-CN&oi=ao">Yongjia Ma</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="#">Hao Li</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="#">Chen Wei</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="#">Xiaofei Gou</a><sup>1</sup>
</span>
<span class="author-block">
<a href="https://scholar.google.com.hk/citations?hl=zh-CN&user=ro8lzsUAAAAJ">Xun Yang</a><sup>3</sup>
</span>
<span class="author-block">
<a href="https://scholar.google.com.hk/citations?hl=zh-CN&user=67fxVzoAAAAJ">Tonghua Su</a><sup>2</sup>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>Space AI, Li Auto,</span>
<span class="author-block"><sup>2</sup>Harbin Institute of Technology,</span>
<span class="author-block"><sup>3</sup>University of Science and Technology of China</span>
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<span>arXiv</span>
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<span>Data(Coming Soon)</span>
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<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
Generating talking face videos from various conditions has
recently become a highly popular research area within gener-
ative tasks. However, building a high-quality face video gen-
eration model requires a well-performing pre-trained back-
bone, a key obstacle that universal models fail to adequately
address. Most existing works rely on universal video or im-
age generation models and optimize control mechanisms, but
they neglect the evident upper bound in video quality due to
the limited capabilities of the backbones, which is a result
of the lack of high-quality human face video datasets. In this
work, we investigate the unsatisfactory results from related
studies, gather and trim existing public talking face video
datasets, and additionally collect and annotate a large-scale
dataset, resulting in a comprehensive, high-quality multiracial
face collection named <b> FaceVid-1K</b> . Using this dataset, we
craft several effective pre-trained backbone models for face
video generation. Specifically, we conduct experiments with
several well-established video generation models, including
text-to-video, image-to-video, and unconditional video gen-
eration, under various settings. We obtain the correspond-
ing performance benchmarks and compared them with those
trained on public datasets to demonstrate the superiority of
our dataset. These experiments also allow us to investigate
empirical strategies for crafting domain-specific video gen-
eration tasks with cost-effective settings. We will make our
curated dataset, along with the pre-trained talking face video
generation models, publicly available as a resource contribu-
tion to hopefully advance the research field.
</p>
</div>
</div>
</div>
<!--/ Abstract. -->
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<h2 class="title is-3">Overview of FaceVid-1K</h2>
<div class="content has-text-justified">
<p>FaceVid-1K contains over 200,000 video clips featuring more than 150,000 unique identities, with 80% representing Asian individuals.</p>
<img src="static\images\figure1_crop.jpg" alt="FaceVid-1K Dataset Overview" style="width: 150%; height: auto;">
</div>
</div>
</div>
</div>
</section>
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<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">Face Video Datasets Comparison</h2>
<p>Compared with other datasets, FaceVid-1K has a larger data volume, competitive quality, and richer attribute annotations.</p>
<div class="content has-text-justified">
<img src="static\images\comparison.png" alt="Comparison" style="width: 150%; height: auto;">
</div>
</div>
</div>
</div>
</section>
<section class="section" style="overflow: auto;">
<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">Statics</h2>
<div class="content has-text-justified">
<p>Distributions of general appearances, hair colors, emotions, actions, ethnicity, and age.</p>
<img src="static\images\figure4.jpg" alt="Statics" style="width: 150%; height: auto;">
</div>
</div>
</div>
</div>
</section>
</section>
<!-- <section class="section">
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<h2 class="title is-3">Visual Effects</h2>
<p>
Using <i>nerfies</i> you can create fun visual effects. This Dolly zoom effect
would be impossible without nerfies since it would require going through a wall.
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As a byproduct of our method, we can also solve the matting problem by ignoring
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</p>
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Loading...
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Using <span class="dnerf">Nerfies</span>, you can re-render a video from a novel
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controls
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<h2 class="title is-3">Related Links</h2>
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<p>
There's a lot of excellent work that was introduced around the same time as ours.
</p>
<p>
<a href="https://arxiv.org/abs/2104.09125">Progressive Encoding for Neural Optimization</a> introduces an idea similar to our windowed position encoding for coarse-to-fine optimization.
</p>
<p>
<a href="https://www.albertpumarola.com/research/D-NeRF/index.html">D-NeRF</a> and <a href="https://gvv.mpi-inf.mpg.de/projects/nonrigid_nerf/">NR-NeRF</a>
both use deformation fields to model non-rigid scenes.
</p>
<p>
Some works model videos with a NeRF by directly modulating the density, such as <a href="https://video-nerf.github.io/">Video-NeRF</a>, <a href="https://www.cs.cornell.edu/~zl548/NSFF/">NSFF</a>, and <a href="https://neural-3d-video.github.io/">DyNeRF</a>
</p>
<p>
There are probably many more by the time you are reading this. Check out <a href="https://dellaert.github.io/NeRF/">Frank Dellart's survey on recent NeRF papers</a>, and <a href="https://github.com/yenchenlin/awesome-NeRF">Yen-Chen Lin's curated list of NeRF papers</a>.
</p>
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<!--/ Concurrent Work. -->
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</section>
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>@inproceedings{Di2024FaceVid1KAL,
title={FaceVid-1K: A Large-Scale High-Quality Multiracial Human Face Video Dataset},
author={Donglin Di and He Feng and Wenzhang Sun and Yongjia Ma and Hao Li and Wei Chen and Xiaofei Gou and Tonghua Su and Xun Yang},
year={2024},
url={https://api.semanticscholar.org/CorpusID:273233717}
}</code></pre>
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