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<h1>EPIC Fields Dataset and Benchmarks</h1>
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<h2 class="section-heading text-uppercase">Watch the Trailer</h2>
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<h2 class="service-heading">EPIC Fields Dataset</h2>
<img src="" width=100%/>
<p>
We introduce EPIC Fields, an augmentation of <a href="http://epic-kitchens.github.io/">EPIC-KITCHENS</a> with 3D camera information.
Similar to other datasets for neural rendering, EPIC Fields removes the complex and expensive step of reconstructing cameras using photogrammetry, and allows researchers to focus on more interesting modeling problems.
We illustrate the challenge of photogrammetry in egocentric videos and propose several technical innovations to address them.
</p>
<p>Compared to other datasets for neural rendering, EPIC Fields is much better tailored to video understanding because it combines nicely with the recently-released <a href="http://epic-kitchens.github.io/VISOR/">VISOR annotations</a>.
Furthermore, it covers the complex and yet increasingly important case of egocentric video understanding.
It also offers new challenges for the neural rendering community, such as modeling long videos with complex dynamic changes.
To further jump start the interest of the community in this area, we also define neural rendering and motion segmentation benchmarks and provide several strong baselines for each, characterizing what is and is not possible today.</p>
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<h4 class="service-heading">Recovered Camera Poses</h4>
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<!-- <img src="static/img/traj_P06_102.jpg" style="width:40%" /> -->
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<p>19M frames in 99 hours of 671 videos, recorded in 45 kitchens.</p>
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<h4 class="service-heading">Dynamic View Synthesis</h4>
<!-- <p>Goal:</p> -->
<div>
<img src="static/img/dynamic_view_synthesis.png" style="width:55%" />
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<div class="col" align="center">
<h4 class="service-heading">Dynamic Object Segmentation</h4>
<!-- <p>Goal:</p> -->
<div>
<img src="static/img/dynamic_object_segmentation.png" style="width:100%" />
</div>
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<div class="col" align="center">
<h4 class="service-heading">Video Object Segmentation</h4>
<!-- <p>Goal:</p> -->
<div>
<img src="static/img/video_object_segmentation.png" style="width:100%" />
</div>
</div>
</div>
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<h4 class="service-heading">When combined with EPIC-KITCHENS annotations, Actions can now be grounded in 3D</h4>
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<img src="static/img/actions_0.jpg" style="width:45%" />
<!-- <img src="static/img/traj_P34_104.jpg" style="width:45%" /> -->
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<!-- <p>19M frames in 99 hours of 703 videos, recorded in 45 kitchens.</p> -->
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</section>
<section id="downloads">
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<h2 class="section-heading text-uppercase">Download Data</h2>
<h4 class="section-subheading">Downloading point clouds and camera poses</h4>
<p style='font-size:150%'>The dataset is now publicly <a href="https://www.dropbox.com/sh/s8y3xm06zjahopq/AADc8tenyIjm1LfIsVeslP18a?dl=0">available for download from here (7.5G).</a></p>
Raw data in COLMAP format:
<ul>
<li>
The dense registered frames in raw COLMAP format can be found at: <a href="https://www.dropbox.com/scl/fo/8li0s3fku8aeaeymd7vqr/h?rlkey=oalzl6cw877d71ljun2ai6iwm&dl=0">here (133G)</a>.
</li>
<li>
The sparse frames including raw COLMAP database can be found at: <a href="https://www.dropbox.com/scl/fo/0wtphqqyp4fu6bd7dhbfs/h?rlkey=ju21graeixi6vpecrf7rqurpt&dl=0">here (91.6G)</a>.
</li>
</ul>
You can also download the files from our servers from Oxford University in case the dropbox servers are too busy:
<ul>
<li>
The <a href="https://thor.robots.ox.ac.uk/epic-fields/json-format.tar.gz"> EPIC Fields dataset</a> (7.5G).
</li>
<li>
The reconstructions with <a href="https://thor.robots.ox.ac.uk/epic-fields/colmap-format-dense-frames.tar">dense registered frames</a> in raw COLMAP format (133G).
</li>
<li>
The reconstructions with <a href="https://thor.robots.ox.ac.uk/epic-fields/colmap-format-sparse-frames.tar"> sparse frames including raw COLMAP database</a> (91.6G).
</li>
</ul>
<p>
You can verify your downloads with the SHA-512 hashes available <a href="https://thor.robots.ox.ac.uk/epic-fields/SHA512SUMS">here</a>.
</p>
<h4 class="section-subheading">Code</h4>
We provide the demo code for visualising the data, and the reconstruction pipeline:
<ul>
<li>
<a href="https://github.com/epic-kitchens/epic-Fields-code">Visualisation and Pipeline</a>
</li>
</ul>
We make the following benchmark codes public, which replicate the EPIC Fields paper's baseline.
<ul>
<li>
<a href="https://github.com/dichotomies/epic-fields-nvs-udos/tree/24d8ba048f0f13b5b3de4cf5d5f3bd2c4505a5d3">Benchmark: NVS and UDOS v1</a> provides the code to
reproduce the results for Task 1 and Task 2.
</li>
<li>
<a href="https://github.com/dichotomies/epic-fields-nvs-udos">Benchmark: NVS and UDOS v2 (10.2.24) </a> provides updated annotations for Task 2 that are further cleaned and extended.
</li>
<li>
<a href="https://github.com/AhmadDarKhalil/epic-fields-vos">Benchmark: VOS</a> contains the baselines for
semi-supervised VOS (EPIC FIELDS).
</li>
</ul>
<h4 class="section-subheading">Paper and Citation</h4>
<p>When using these annotations, cite our paper (<a href="http://arxiv.org/abs/2306.08731">preprint now available on ArXiv</a>):</p>
<pre class="bibtex"><code>@inproceedings{EPICFields2023,
title={{EPIC Fields}: {M}arrying {3D} {G}eometry and {V}ideo {U}nderstanding},
author={Tschernezki, Vadim and Darkhalil, Ahmad and Zhu, Zhifan and Fouhey, David and Larina, Iro and Larlus, Diane and Damen, Dima and Vedaldi, Andrea},
booktitle = {Proceedings of the Neural Information Processing Systems (NeurIPS)},
year = {2023}
} </code></pre>
Also cite the <a href="https://link.springer.com/content/pdf/10.1007/s11263-021-01531-2.pdf">EPIC-KITCHENS-100</a> paper where the videos originate:
<pre class="bibtex"><code>@ARTICLE{Damen2022RESCALING,
title={Rescaling Egocentric Vision: Collection, Pipeline and Challenges for EPIC-KITCHENS-100},
author={Damen, Dima and Doughty, Hazel and Farinella, Giovanni Maria and and Furnari, Antonino
and Ma, Jian and Kazakos, Evangelos and Moltisanti, Davide and Munro, Jonathan
and Perrett, Toby and Price, Will and Wray, Michael},
journal = {International Journal of Computer Vision (IJCV)},
year = {2022},
volume = {130},
pages = {33–55},
Url = {https://doi.org/10.1007/s11263-021-01531-2}
} </code></pre>
</div>
</div>
<div class="row">
<div class="col-md-12">
<h4 class="section-subheading">Disclaimer </h4>
<p>The underlying data that power EPIC Fields, EPIC-KITCHENS-100, were collected as a tool for research in computer vision. The dataset may have unintended biases (including those of a societal, gender or racial nature).</p>
</div>
</div>
<div class="row">
<div class="col-md-12">
<h4 class="section-subheading">Copyright <img alt="Creative Commons License" style="border-width:1px;float:left;margin-right:15px;margin-bottom:0px;" src="https://i.creativecommons.org/l/by-nc/3.0/88x31.png"/></h4>
<p>
The EPIC Fields dataset is copyright by us and published under the <a rel="license" href="https://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-NonCommercial 4.0 International</a> License. This means that you must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not use the material for commercial purposes.
</p>
<p>For commercial licenses of EPIC-KITCHENS, email us at <a href="mailto:[email protected]">[email protected]</a></p>
</div>
</div>
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</section>
<section id="team" class="bg-light">
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<h2 class="section-heading text-uppercase">The Team</h2>
</div>
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<div class="col-md-12 text-center">
<p>EPIC Fields is the result of a collaboration of the Universities of <a href="https://www.ox.ac.uk">Oxford</a>, <a href='http://www.bristol.ac.uk/'>Bristol</a>, Michigan and NAVER LABS Europe</p>
</div>
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<div class="team-member">
<img class="mx-auto rounded-circle" src="static/img/profile/vt.png" />
<h4><a href="https://scholar.google.com/citations?user=-Udk-5IAAAAJ&hl=en">Vadim Tschernezki*</a></h4>
<h6 class="text-muted">University of Oxford</h6>
</div>
</div> <!--Vadim-->
<div class="col-md-3">
<div class="team-member">
<img class="mx-auto rounded-circle" src="static/img/profile/ad.png" />
<h4><a href="https://uk.linkedin.com/in/ahmad-dar-khalil-88b9b7108">Ahmad Darkhalil*</a></h4>
<h6 class="text-muted">University of Bristol</h6>
</div>
</div> <!--Ahmad-->
<div class="col-md-3">
<div class="team-member">
<img class="mx-auto rounded-circle" src="static/img/profile/zz.jpg"/>
<h4><a href="https://zhifanzhu.github.io">Zhifan Zhu*</a></h4>
<h6 class="text-muted">University of Bristol</h6>
</div>
</div> <!--Zhifan-->
<div class="col-md-3">
<div class="team-member">
<img class="mx-auto rounded-circle" src="static/img/profile/df.jpg" />
<h4><a href="https://web.eecs.umich.edu/~fouhey/">David Fouhey</a></h4>
<h6 class="text-muted">University of Michigan</h6>
</div>
</div> <!--David-->
<div class="col-md-3">
<div class="team-member">
<img class="mx-auto rounded-circle" src="static/img/profile/il.png" />
<h4><a href="https://scholar.google.de/citations?user=n9nXAPcAAAAJ&hl=en">Iro Laina</a></h4>
<h6 class="text-muted">University of Oxford</h6>
</div>
</div> <!--Iro-->
<div class="col-md-3">
<div class="team-member">
<img class="mx-auto rounded-circle" src="static/img/profile/dl.jpg" />
<h4><a href="https://dlarlus.github.io">Diane Larlus</a></h4>
<h6 class="text-muted">NAVER LABS Europe</h6>
</div>
</div> <!--Diane-->
<div class="col-md-3">
<div class="team-member">
<img class="mx-auto rounded-circle" src="static/img/profile/dd.jpg" />
<h4><a href="https://dimadamen.github.io">Dima Damen</a></h4>
<h6 class="text-muted">University of Bristol</h6>
</div>
</div> <!--Dima-->
<div class="col-md-3">
<div class="team-member">
<img class="mx-auto rounded-circle" src="static/img/profile/av.jpg" />
<h4><a href="https://www.robots.ox.ac.uk/~vedaldi/">Andrea Vedaldi</a></h4>
<h6 class="text-muted">University of Oxford</h6>
</div>
</div> <!--Andrea-->
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<h2 class="section-heading text-uppercase">Research Funding</h2>
<div class="text-muted">
<p> The work on EPIC Fields was supported by:</p>
<ul class="text-muted">
<li>UKRI Engineering and Physical Sciences Research Council (EPSRC) Program Grant Visual AI (EP/T028572/1)</li>
<li>V. Tschernezki and D.Larlus are supported by Naver Labs.</li>
<li>A. Darkhalil is supported by EPSRC DTP program.</li>
<li>Z. Zhu is supported by UoB-CSC Scholarship.</li>
<li>I. Laina and A. Vedaldi are supported by ERC-CoG UNION 101001212.</li>
<li>D. Damen is supported by EPSRC Fellowship UMPIRE~EP/T004991/1. </li>
</ul>
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