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<!DOCTYPE html>
<html lang="en">
<head>
<meta http-equiv="Content-Type" content="text/html;charset=UTF-8" />
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<meta
name="description"
content="A large-scale first-person video dataset, supporting research in multi-modal machine perception for daily life activity"
/>
<meta
name="keywords"
content="video dataset, machine perception, machine-learning, research"
/>
<title>Egocentric 4D Perception (EGO4D)</title>
<link rel="stylesheet" href="assets/css/style.css" />
<link rel="icon" href="./assets/images/ego4d-icon.ico" />
<!-- Latest font-awesome css for icons -->
<link
rel="stylesheet"
href="https://use.fontawesome.com/releases/v5.15.4/css/all.css"
integrity="sha384-DyZ88mC6Up2uqS4h/KRgHuoeGwBcD4Ng9SiP4dIRy0EXTlnuz47vAwmeGwVChigm"
crossorigin="anonymous"
/>
<!-- BS3 compiled and minified CSS -->
<link
rel="stylesheet"
href="https://maxcdn.bootstrapcdn.com/bootstrap/3.4.1/css/bootstrap.min.css"
/>
<!-- jQuery library -->
<script src="https://ajax.googleapis.com/ajax/libs/jquery/3.5.1/jquery.min.js"></script>
<!-- Latest compiled JavaScript for BS3 -->
<script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.4.1/js/bootstrap.min.js"></script>
<script src="assets/scripts/form-submission.js"></script>
<script type="text/javascript">
function openTabbedContent(element) {
var whereTo = $(element).attr("goto"),
tabs = $("#benchmarks li"),
links = tabs.find("a[href='#" + whereTo + "']");
links[0].click();
}
</script>
</head>
<body>
<header>
<nav id="nav-bar" class="navbar nav-dropdown navbar-expand-lg">
<div id="nav-links" class="container-fluid">
<button
class="navbar-toggler"
type="button"
data-toggle="collapse"
data-target="#nav-menu"
aria-controls="nav-menu"
aria-expanded="false"
aria-label="Toggle navigation"
>
<div class="hamburger">
<span></span>
<span></span>
<span></span>
<span></span>
</div>
</button>
<div id="nav-logo">
<img id="logo" src="assets/images/ego-4d-01.png" alt="Logo" />
</div>
<div id="nav-menu" class="collapse">
<ul id="nav-menu-list" data-app-modern-menu="true">
<li class="nav-item">
<a href="index.html" class="nav-link">Home</a>
</li>
<li class="nav-item">
<a href="#challenges" class="nav-link">Challenges</a>
</li>
<li class="nav-item">
<a href="#benchmarks" class="nav-link">Benchmarks</a>
</li>
<li class="nav-item">
<a href="#consortium" class="nav-link">Consortium</a>
</li>
<li class="nav-item">
<a href="#qa" class="nav-link">Questions/Answers</a>
</li>
<li class="nav-item">
<a href="#" class="nav-link">Contacts</a>
</li>
</ul>
<div id="social-links">
<a href="#" class="icon-wrap">
<i class="fab fa-instagram"></i>
</a>
<a href="#" class="icon-wrap">
<i class="fab fa-facebook-f"></i>
</a>
</div>
</div>
<div id="discover">
<a
href="#"
class="button-link themed-button button-outline nav-link"
>Discover</a
>
</div>
</div>
</nav>
</header>
<section id="parallax-image">
<div class="container">
<div class="row justify-center">
<div class="col-lg-6 align-center">
<!-- <div>
<a class="btn large-button button-link btn-translucent" href="#"
>Watch the Trailer</a
>
</div>-->
</div>
</div>
</div>
</section>
<section class="container-fluid">
<div
id="carousel"
class="carousel slide pointer-event"
role="listbox"
data-pause="true"
data-keyboard="false"
data-ride="carousel"
data-interval="10000"
>
<div id="carousel-bg"></div>
<!-- Wrapper for slides -->
<div class="carousel-inner">
<div class="item active">
<div class="row slide">
<div
class="
col-xs-12 col-md-4 col-lg-4 col-md-offset-1 col-lg-offset-1
slide-content
"
>
<h2 class="title">MASSIVE SCALE</h2>
<p class="slide-text">
Ego4D is a massive-scale Egocentric dataset containing over
2,644 hours of unscripted, daily-life videos shot from a first
person perspective. To ensure its realism, the dataset was
collected from 13 worldwide locations in 7 different countries
by wearers of all ages and disciplines. Ego4D is a diverse and
challenging dataset that will push the frontier of
first-person vision.
</p>
</div>
<div class="col-xs-12 col-md-7 col-lg-7 slide-image-wrap">
<img
class="img-responsive slide-image"
src="assets/images/tsne.png"
alt="Placeholder image 3"
/>
</div>
</div>
</div>
<div class="item">
<div class="row slide">
<div
class="
col-xs-12 col-md-4 col-lg-4 col-md-offset-1 col-lg-offset-1
slide-content
"
>
<h2 class="title">DIVERSE</h2>
<p class="slide-text">
The dataset is diverse in its geographic covereage,
activities, modalities, ...
</p>
</div>
<div class="col-xs-12 col-md-7 col-lg-7 slide-image-wrap">
<img
class="img-responsive slide-image"
src="assets/images/Ego4D-Collage.png"
alt="Scenario Distribution in EGO4D"
/>
</div>
</div>
</div>
<div class="item">
<div class="row slide">
<div
class="
col-xs-12 col-md-4 col-lg-4 col-md-offset-1 col-lg-offset-1
slide-content
"
>
<h2 class="title">PRIVACY/ETHICS</h2>
<p class="slide-text">
From its inception, high standards of privacy and ethics were
paramount for the Ego4D project. Each partner ensured that all
collected video and audio met University and consortium
requirements. These include (but are not limited to):
Complying with laws and regulations within country of
collection; Respecting the right of others; and De-identify
identifiable information using robust manual and automatic
processes.
</p>
</div>
<div class="col-xs-12 col-md-7 col-lg-7 slide-image-wrap">
<img
class="img-responsive slide-image"
src="assets/images/anony.png"
alt="Placeholder image 3"
/>
</div>
</div>
</div>
</div>
<!-- Left and right controls -->
<div id="carousel-controls">
<a href="#carousel" data-slide="prev">
<span id="left-control" class="carousel-control" aria-hidden="true"
>←</span
>
<span class="sr-only">Previous</span>
</a>
<a href="#carousel" data-slide="next">
<span id="right-control" class="carousel-control" aria-hidden="true"
>→</span
>
<span class="sr-only">Next</span>
</a>
</div>
</div>
</section>
<section id="challenges">
<div class="container">
<div class="row card-row">
<div class="col-xs-12 col-sm-6 col-lg-3 title">
<h2 class="title">Challenges</h2>
</div>
<div class="col-xs-12 col-sm-6 col-lg-3 card-block">
<div class="card-content">
<div class="card-image">
<img
src="assets/images/episodic.png"
alt="Placeholder image 5"
/>
</div>
<div class="card-info">
<h3 class="card-title">Episodic Memory</h3>
</div>
</div>
</div>
<div class="col-xs-12 col-sm-6 col-lg-3 card-block">
<div class="card-content">
<div class="card-image">
<img
src="assets/images/forecasting.png"
alt="Placeholder image 4"
/>
</div>
<div class="card-info">
<h3 class="card-title">Forecasting</h3>
</div>
</div>
</div>
<div class="col-xs-12 col-sm-6 col-lg-3 card-block">
<div class="card-content">
<div class="card-image">
<img src="assets/images/h+o.png" alt="Placeholder image 3" />
</div>
<div class="card-info">
<h3 class="card-title">Hand-Object Interactions</h3>
</div>
</div>
</div>
<div class="col-xs-12 col-sm-6 col-lg-3 card-block empty-block"></div>
<div class="col-xs-12 col-sm-6 col-lg-3 card-block">
<div class="card-content">
<div class="card-image">
<img src="assets/images/a-vD.png" alt="Placeholder image 6" />
</div>
<div class="card-info">
<h3 class="card-title">AV Diarization</h3>
</div>
</div>
</div>
<div class="col-xs-12 col-sm-6 col-lg-3 card-block">
<div class="card-content">
<div class="card-image">
<img src="assets/images/social.png" alt="Placeholder image 7" />
</div>
<div class="card-info">
<h3 class="card-title">Social</h3>
</div>
</div>
</div>
</div>
</div>
</section>
<section id="benchmarks">
<div class="container">
<div class="row">
<div id="tab-header" class="align-center">
<h3 class="title no-margin">Benchmarks</h3>
</div>
</div>
<div class="content-row">
<div id="tabbed-content">
<ul class="nav nav-tabs">
<li id="episodic-memory" class="nav-link">
<a data-toggle="tab" href="#menu1">Episodic Memory</a>
</li>
<li id="forecasting" class="nav-link">
<a data-toggle="tab" href="#menu2">Forecasting</a>
</li>
<li id="hoi" class="nav-link active">
<a data-toggle="tab" href="#menu3">HOI</a>
</li>
<li id="av-diarization" class="nav-link">
<a data-toggle="tab" href="#menu4">AV Diarization</a>
</li>
<li id="social" class="nav-link">
<a data-toggle="tab" href="#menu5">Social</a>
</li>
</ul>
<div class="tab-content">
<div id="menu1" class="tab-pane fade">
<div class="row">
<div class="col-lg-6 tab-wrap">
<div class="tab-image nav-tab-image">
<img src="assets/images/episodic.png" alt="" title="" />
</div>
<div class="tab-card-content">
<h5 class="card-title">Episodic Memory</h5>
<p class="card-text">
<em>Where is my X?</em><br />
Egocentric video gives a recording of a wearer's daily
life, and can be used augment human memory on demand.
Such a system might be able to remind a user where they
left their keys, if they added salt to a recipe, or
recall events they attended.
</p>
</div>
</div>
<div class="col-lg-6 tab-wrap">
<div class="tab-image nav-tab-image">
<img src="assets/images/episodic.png" alt="" title="" />
</div>
<div class="tab-card-content">
<h5 class="card-title">Querying Memory</h5>
<p class="card-text">
There are three different tasks within this benchmark
based on the input type used to query the memory: visual
query (i.e. find the location given an image of keys),
textual query ("how many cups of sugar did I add?"), and
a moment query (find all instances of "When did I play
with the dog").
</p>
</div>
</div>
<div class="col-lg-6 tab-wrap">
<div class="tab-image nav-tab-image">
<img src="assets/images/episodic.png" alt="" title="" />
</div>
<div class="tab-card-content">
<h5 class="card-title">Construction of Queries</h5>
<p class="card-text">
For the language queries, a set of templates were
designed which annotators used to write questions for
the task. Examples include "what is the state of object
X?" or "where is object X after event Y"? These were
then re-written for variety.
</p>
</div>
</div>
<div class="col-lg-6 tab-wrap">
<div class="tab-image nav-tab-image">
<img src="assets/images/episodic.png" alt="" title="" />
</div>
<div class="tab-card-content">
<h5 class="card-title">Recalling Lives</h5>
<p class="card__text">
Given the broad nature of this benchmark, there isn't a
subset of activities that were focused on within this
task, leading to a realistic and challenging benchmark.
</p>
</div>
</div>
</div>
</div>
<div id="menu2" class="tab-pane fade">
<div class="row">
<div class="col-lg-6 tab-wrap">
<div class="tab-image nav-tab-image">
<img
src="assets/images/forecasting.png"
alt=""
title=""
/>
</div>
<div class="tab-card-content">
<h5 class="card-title">Forecasting</h5>
<p class="card-text">
<em>What will I do next?</em><br />
Predicting the future is a critical skill for systems to
provide timely assistance for users. With a myriad of
long-form, unscripted videos, Ego4D provides an
interesting challenge for this task.
</p>
</div>
</div>
<div class="col-lg-6 tab-wrap">
<div class="tab-image nav-tab-image">
<img
src="assets/images/forecasting.png"
alt=""
title=""
/>
</div>
<div class="tab-card-content">
<h5 class="card-title">Where Will I Go?</h5>
<p class="card-text">
Two tasks each consider where the camera wearer will go
within the scene (if they picked up an empty kettle it
is likely they will go to the sink) and the wearer's
hands (picking up a knife will lead to a chopping
motion).
</p>
</div>
</div>
<div class="col-lg-6 tab-wrap">
<div class="tab-image nav-tab-image">
<img
src="assets/images/forecasting.png"
alt=""
title=""
/>
</div>
<div class="tab-card-content">
<h5 class="card-title">Short and Long Term</h5>
<p class="card-text">
The other two tasks consider the short term future as
the next object interaction that will take place and a
countdown towards it taking place as well as the long
term - what are the next possible sequence of actions?
</p>
</div>
</div>
<div class="col-lg-6 tab-wrap">
<div class="tab-image nav-tab-image">
<img
src="assets/images/forecasting.png"
alt=""
title=""
/>
</div>
<div class="tab-card-content">
<h5 class="card-title">Data for Prophets</h5>
<p class="card__text">
The data for this challenge has been selected from a
diverse set of activities containing many human-object
interactions and movements such as brick making, cooking
or carpentry.
</p>
</div>
</div>
</div>
</div>
<div id="menu3" class="tab-pane fade in active">
<div class="row">
<div class="col-lg-6 tab-wrap">
<div class="tab-image nav-tab-image">
<img src="assets/images/h+o.png" alt="" title="" />
</div>
<div class="tab-card-content">
<h5 class="card-title">Hand + Object Interaction</h5>
<p class="card-text">
<em>How do objects change during interactions?</em
><br />
Going beyond Action Recognition, this benchmark follows
when and how an object is changed during its interaction
- only possible through a first person Viewpoint.
</p>
</div>
</div>
<div class="col-lg-6 tab-wrap">
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<img src="assets/images/h+o.png" alt="" title="" />
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<div class="tab-card-content">
<h5 class="card-title">Changes of State</h5>
<p class="card-text">
We capture annotatios of objects, as they transform,
temporally, spatially and semantically - an onion might
be minced. These are represented by three different
tasks in the benchmark: Point-of-no-return Temporal
Localisation, Active Object Detection and State-Change
Classification.
</p>
</div>
</div>
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<img src="assets/images/h+o.png" alt="" title="" />
</div>
<div class="tab-card-content">
<h5 class="card-title">Pre/Post Conditions</h5>
<p class="card-text">
Each annotation has been labelled with prior states
(i.e. the prior condition) and posterior states as well
as the point of no return (PNR) in which the state
change is triggered.
</p>
</div>
</div>
<div class="col-lg-6 tab-wrap">
<div class="tab-image nav-tab-image">
<img src="assets/images/h+o.png" alt="" title="" />
</div>
<div class="tab-card-content">
<h5 class="card-title">World of Interactions</h5>
<p class="card__text">
The data for this challenge has been selected from
activities with a high level of hand-object interactions
such as knitting, capentry and baking.
</p>
</div>
</div>
</div>
</div>
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<h5 class="card-title">Audio-Visual Diarization</h5>
<p class="card-text">
<em>Who said what, and when?</em><br />
Conversations are egocentric in nature, and a
human-in-the-loop AI requires skills such as detecting
speakers and recognition in order to communicate
effectively.
</p>
</div>
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<img src="assets/images/a-vD.png" alt="" title="" />
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<div class="tab-card-content">
<h5 class="card-title">Looking for Conversation</h5>
<p class="card-text">
This benchmark contains 2 different tasks focused on
visual data: detection of the speaker and recognition of
the speaker. Note that identities are anonymized to
match consortium guidelines.
</p>
</div>
</div>
<div class="col-lg-6 tab-wrap">
<div class="tab-image nav-tab-image">
<img src="assets/images/a-vD.png" alt="" title="" />
</div>
<div class="tab-card-content">
<h5 class="card-title">Hearing the Words</h5>
<p class="card-text">
The benchmark also includes 2 tasks for the audio
modality: diaraization/temporal extent of the sentences
spoken and the transcription of the coversation.
</p>
</div>
</div>
<div class="col-lg-6 tab-wrap">
<div class="tab-image nav-tab-image">
<img src="assets/images/a-vD.png" alt="" title="" />
</div>
<div class="tab-card-content">
<h5 class="card-title">Much Ado About Talking</h5>
<p class="card__text">
With this task focused on conversations, scenarios were
chosen which included multiple participants interacting
together, such as eating, playing games or setting up
tents.
</p>
</div>
</div>
</div>
</div>
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</div>
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<h5 class="card-title">Social Interactions</h5>
<p class="card-text">
<em>Who is attending to whom?</em><br />In addition to
spoken language, communication depends on nonverbal cues
such as focus or facial expressions. Egocentric data
perfectly captures interactions like these due to the
point of view.
</p>
</div>
</div>
<div class="col-lg-6 tab-wrap">
<div class="tab-image nav-tab-image">
<img src="assets/images/social.png" alt="" title="" />
</div>
<div class="tab-card-content">
<h5 class="card-title">More than Conversation</h5>
<p class="card-text">
Social extends the Audio-Visual Diaraization benchmark
towardsunderstanding the conversations of a social group
over a longer period of time for specific tasks.
</p>
</div>
</div>
<div class="col-lg-6 tab-wrap">
<div class="tab-image nav-tab-image">
<img src="assets/images/social.png" alt="" title="" />
</div>
<div class="tab-card-content">
<h5 class="card-title">Talking and Listening</h5>
<p class="card-text">
This benchmark includes two different tasks focused on
when a person is <em>Looking at Me</em> and when a
person is <em>Talking to Me</em>.
</p>
</div>
</div>
<div class="col-lg-6 tab-wrap">
<div class="tab-image nav-tab-image">
<img src="assets/images/social.png" alt="" title="" />
</div>
<div class="tab-card-content">
<h5 class="card-title">Unique Interactions</h5>
<p class="card__text">
The data within the Social Interaction task was
collected specifically for this task in mind with
multi-user scenarios such as social deduction games,
eating/drinking and playing basketball.
</p>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</section>
<section id="consortium">
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src="assets/images/cmu-wordmark-stacked-r.png"
alt=""
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<img src="assets/images/NUS.jpeg" alt="" title="" />
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src="assets/images/KAUST_logo_for_Digital_Media_Large-01.png"
alt=""
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/>
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src="assets/images/University_of_Tokyo_Logo-700x181.png"
alt=""
title=""
/>
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</div>
<div id="consortium-text" class="col-md-3 col-lg-4 col-xs-12">
<h3 class="title">EGO4D Consortium</h3>
<p>
EGO4D was collected by a consortium of 14 universities and
Facebook AI Research.
</p>
</div>
</div>
</div>
</section>
<section id="qa">
<div class="container">
<div class="row">
<div class="col-xs-12 align-center">
<h3 class="title large-text">QUESTIONS / ANSWERS</h3>
</div>
</div>
<div class="timelines-container">
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>
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<h4 class="small-title">
What to cite referencing this effort?
</h4>
<p>
If using the dataset, annotations or inspiration from this
work, cite:
</p>
<pre class="bibtex"><code>@ARTICLE{EGO4D2021,
title={Ego4D: Unscripted First-Person Video from Around the World and a Benchmark Suite for Egocentric Perception},
author={TBD},
journal = {CoRR},
volume = {TBD},
year = {2021},
ee = {http://arxiv.org/abs/??},
} </code></pre>
</div>
</div>
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<h4 class="small-title">Where can I find more details?</h4>
<p>
The dataset collection and challenges are described in this
ArXiv paper (Oct 2021)
</p>
</div>
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<h4 class="small-title">What MetaData is available?</h4>
<p>
For each video, we provide information about the collecting
partner/university, date of recording, recording equipment, as
well as video parts when the video is made up of smaller
chunks. Information about the avalability of IMU, Audio and
whether videos have been redacted are also included. Refer to
README for formats of metadata.
</p>
</div>
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<h4 class="small-title">Who collected this data?</h4>
<p>The data was collected through informed consent...</p>
</div>
</div>
</div>
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<h4 class="small-title">
Does the data contain identifying information of individuals?
</h4>
<p>
Only when consent has been collected from participants, the
data will contain faces and other identifying information. The
collecting partner holds consent forms in this case. For all
other videos, data has been de-identified pre-release.
Original footage has been deleted and are no longer
accessible. Refer to ArXiv (Sec ?) for details of our
de-identification pipeline.
</p>
</div>
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<h4 class="small-title">
What resolution and framerate are available?
</h4>
<p>
This depends on equipment. We release all footage using the
native resolution, but also offer a standardised frame-rate
version of 30fps for ease of use. All benchmark results use
the standardised version.
</p>
</div>
</div>
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<h4 class="small-title">
How can I participate in the benchmarks?
</h4>
<p>
Training and validatioon annotations are now publicly
available. Test servers will be opened early next year for the
first round of benchmarks. Results are expected to be
announced in CVPR 2022. Revisit this webpage for further
information.
</p>
</div>
</div>
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</div>
</section>
<section id="people">
<div class="container">
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<div class="title-block align-center">
<h3 class="title large-text">EGO4D Team</h3>
</div>
<div class="col-lg-4">
<h4 class="card-title">
Carnegie Mellon University, Pittsburgh, U.S.
<a href="https://www.cmu.edu"
><i class="fas fa-link"></i
></a>
</h4>
<ul>
<li>
Kris Kitani (PI)
<a href="https://www.cs.cmu.edu/~kkitani/">
<i class="fas fa-link"></i>
</a>
<a href="https://twitter.com/kkitani">
<i class="fab fa-twitter"></i>
</a>
</li>
<li>
Xingyu Liu
<a href="https://xingyul.github.io/">
<i class="fas fa-link"></i>
</a>
<a href="https://www.linkedin.com/in/xing-yu-liu/">
<i class="fab fa-linkedin"></i>
</a>
</li>
<li>
Qichen Fu
<a href="https://fuqichen1998.github.io/">
<i class="fas fa-link"></i>
</a>
<a href="https://www.linkedin.com/in/qichen-fu/">
<i class="fab fa-linkedin"></i>
</a>
</li>
<li>Sean Crane</li>
<li>
Xuhua Huang
<a href="https://xuhuaking.github.io/">
<i class="fas fa-link"></i>