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content="Deformable Neural Radiance Fields creates free-viewpoint portraits (nerfies) from casually captured videos.">
<meta name="keywords" content="Nerfies, D-NeRF, NeRF">
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<title>Nerfies: Deformable Neural Radiance Fields</title>
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<h1 class="title is-1 publication-title">Towards Lifelong Dialogue Agents via Timeline-based Memory Management</h1>
<!-- <div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://keunhong.com">Keunhong Park</a><sup>1</sup>,</span>
<span class="author-block">
<a href="https://utkarshsinha.com">Utkarsh Sinha</a><sup>2</sup>,</span>
<span class="author-block">
<a href="https://jonbarron.info">Jonathan T. Barron</a><sup>2</sup>,
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<span class="author-block">
<a href="http://sofienbouaziz.com">Sofien Bouaziz</a><sup>2</sup>,
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<a href="https://www.danbgoldman.com">Dan B Goldman</a><sup>2</sup>,
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<a href="https://homes.cs.washington.edu/~seitz/">Steven M. Seitz</a><sup>1,2</sup>,
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<span class="author-block">
<a href="http://www.ricardomartinbrualla.com">Ricardo Martin-Brualla</a><sup>2</sup>
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<span class="author-block"><sup>1</sup>University of Washington,</span>
<span class="author-block"><sup>2</sup>Google Research</span>
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<h2 class="subtitle has-text-centered">
<span class="dnerf">
You can download the codes and the TeaBag dataset
<a href="https://github.com/kimnamssya/Theanine" target="_blank" rel="noopener noreferrer">
here
</a>
and
<a href="https://huggingface.co/datasets/LangAGI-Lab/teafarm" target="_blank" rel="noopener noreferrer">
here
</a>.
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<h2 class="title is-3">Abstract</h2>
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<p>
We present the first method capable of photorealistically reconstructing a non-rigidly
deforming scene using photos/videos captured casually from mobile phones.
</p>
<p>
Our approach augments neural radiance fields
(NeRF) by optimizing an
additional continuous volumetric deformation field that warps each observed point into a
canonical 5D NeRF.
We observe that these NeRF-like deformation fields are prone to local minima, and
propose a coarse-to-fine optimization method for coordinate-based models that allows for
more robust optimization.
By adapting principles from geometry processing and physical simulation to NeRF-like
models, we propose an elastic regularization of the deformation field that further
improves robustness.
</p>
<p>
We show that <span class="dnerf">Nerfies</span> can turn casually captured selfie
photos/videos into deformable NeRF
models that allow for photorealistic renderings of the subject from arbitrary
viewpoints, which we dub <i>"nerfies"</i>. We evaluate our method by collecting data
using a
rig with two mobile phones that take time-synchronized photos, yielding train/validation
images of the same pose at different viewpoints. We show that our method faithfully
reconstructs non-rigidly deforming scenes and reproduces unseen views with high
fidelity.
</p>
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<h2 class="title is-3">Supplementary video for our framework.</h2>
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<h2 class="title">BibTeX</h2>
<pre><code>@article{park2021nerfies,
author = {Park, Keunhong and Sinha, Utkarsh and Barron, Jonathan T. and Bouaziz, Sofien and Goldman, Dan B and Seitz, Steven M. and Martin-Brualla, Ricardo},
title = {Nerfies: Deformable Neural Radiance Fields},
journal = {ICCV},
year = {2021},
}</code></pre>
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