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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="description"
content="VIRL: Self-Supervised Visual Graph Inverse Reinforcement Learning.">
<meta name="keywords" content="Learning from video, inverse reinforcement learning">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>VIRL: Self-Supervised Visual Graph Inverse Reinforcement Learning</title>
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</head>
<body>
<section class="hero">
<div class="hero-body">
<div class="container is-max-desktop">
<div class="columns is-centered">
<div class="column has-text-centered">
<h1 class="title is-1 publication-title">VIRL: Self-Supervised Visual Graph Inverse Reinforcement Learning</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://leihhhuang.github.io/">Lei Huang</a><sup>1</sup>,</span>
<span class="author-block">
<a href="https://yesandy.github.io/andycai/">Weijia Cai</a><sup>1</sup>,</span>
<span class="author-block">
<a href="https://cs.brown.edu/people/grad/zzhu92/">Zihan Zhu</a><sup>2</sup>,
</span>
<span class="author-block">
<a href="https://engineering.nyu.edu/faculty/chen-feng">Chen Feng</a><sup>3</sup>,
</span>
<span class="author-block">
<a href="https://helge.rhodin.de/">Helge Rhodin</a><sup>4</sup>,
</span>
<span class="author-block">
<a href="https://www.civil.columbia.edu/content/zhengbo-zou">Zhengbo Zou</a><sup>1</sup>,
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>Columbia University,</span>
<span class="author-block"><sup>2</sup>Brown University,</span>
<span class="author-block"><sup>1</sup>New York University,</span>
<span class="author-block"><sup>2</sup>University of British Columbia</span>
</div>
<div class="column has-text-centered">
<div class="publication-links">
<!-- PDF Link. -->
<span class="link-block">
<a href="https://openreview.net/pdf?id=fDRO4NHEwZ"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fas fa-file-pdf"></i>
</span>
<span>OpenReview</span>
</a>
</span>
<!-- <span class="link-block">
<a href="https://arxiv.org/abs/2011.12948"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="ai ai-arxiv"></i>
</span>
<span>arXiv</span>
</a>
</span> -->
<!-- Video Link. -->
<span class="link-block">
<a href="https://leihhhuang.github.io/VIRL/"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fab fa-youtube"></i>
</span>
<span>Video</span>
</a>
</span>
<!-- Code Link. -->
<span class="link-block">
<a href="https://leihhhuang.github.io/VIRL/"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fab fa-github"></i>
</span>
<span>Code coming soon</span>
</a>
</div>
</div>
</div>
</div>
</div>
</div>
</section>
<section class="hero teaser">
<div class="container is-max-desktop">
<div class="hero-body">
<video id="teaser" autoplay muted loop playsinline height="100%">
<source src="./static/videos/teaser.mp4"
type="video/mp4">
</video>
<h2 class="subtitle has-text-centered">
<span class="dnerf">Nerfies</span> turns selfie videos from your phone into
free-viewpoint
portraits.
</h2>
</div>
</div>
</section>
<section class="hero is-light is-small">
<div class="hero-body">
<div class="container">
<div id="results-carousel" class="carousel results-carousel">
<div class="item item-steve">
<video poster="" id="steve" autoplay controls muted loop playsinline height="100%">
<source src="./static/videos/steve.mp4"
type="video/mp4">
</video>
</div>
<div class="item item-chair-tp">
<video poster="" id="chair-tp" autoplay controls muted loop playsinline height="100%">
<source src="./static/videos/chair-tp.mp4"
type="video/mp4">
</video>
</div>
<div class="item item-shiba">
<video poster="" id="shiba" autoplay controls muted loop playsinline height="100%">
<source src="./static/videos/shiba.mp4"
type="video/mp4">
</video>
</div>
<div class="item item-fullbody">
<video poster="" id="fullbody" autoplay controls muted loop playsinline height="100%">
<source src="./static/videos/fullbody.mp4"
type="video/mp4">
</video>
</div>
<div class="item item-blueshirt">
<video poster="" id="blueshirt" autoplay controls muted loop playsinline height="100%">
<source src="./static/videos/blueshirt.mp4"
type="video/mp4">
</video>
</div>
<div class="item item-mask">
<video poster="" id="mask" autoplay controls muted loop playsinline height="100%">
<source src="./static/videos/mask.mp4"
type="video/mp4">
</video>
</div>
<div class="item item-coffee">
<video poster="" id="coffee" autoplay controls muted loop playsinline height="100%">
<source src="./static/videos/coffee.mp4"
type="video/mp4">
</video>
</div>
<div class="item item-toby">
<video poster="" id="toby" autoplay controls muted loop playsinline height="100%">
<source src="./static/videos/toby2.mp4"
type="video/mp4">
</video>
</div>
</div>
</div>
</div>
</section>
<section class="section">
<div class="container is-max-desktop">
<!-- Abstract. -->
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
We present an inverse reinforcement learning method capable of learning reward functions from videos for the demonstrated task and unseen variant tasks.
</p>
<p>
Learning dense reward functions from unlabeled videos for reinforcement learning exhibits scalability due to the vast diversity and quantity of video resources. Recent works use visual features or graph abstractions in videos to measure task progress as rewards, which either deteriorate in unseen domains or capture spatial information while overlooking visual details. We propose <b>V</b>isual-Graph <b>I</b>nverse <b>R</b>einforcement <b>L</b>earning (<b>VIRL</b>), a self-supervised method that synergizes low-level visual features and high-level graph abstractions from frames to graph representations for reward learning. VIRL utilizes a visual encoder that extracts object-wise features for graph nodes and a graph encoder that derives properties from graphs constructed from detected objects in each frame. The encoded representations are enforced to align videos temporally and reconstruct in-scene objects. The pretrained visual graph encoder is then utilized to construct a dense reward function for policy learning by measuring latent distances between current frames and the goal frame. Our empirical evaluation on the X-MAGICAL and Robot Visual Pusher benchmark demonstrates that VIRL effectively handles tasks necessitating both granular visual attention and broader global feature consideration, and exhibits robust generalization to <i>extrapolation</i> tasks and domains not seen in demonstrations. Our policy for the robotic task also achieves the highest success rate in real-world robot experiments.
</p>
</div>
</div>
</div>
<!--/ Abstract. -->
<!-- Paper video. -->
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Video</h2>
<div class="publication-video">
<iframe src="https://www.youtube.com/embed/MrKrnHhk8IA?rel=0&showinfo=0"
frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
</div>
</div>
</div>
<!--/ Paper video. -->
</div>
</section>
<section class="section">
<div class="container is-max-desktop">
<div class="columns is-centered">
<!-- Visual Effects. -->
<div class="column">
<div class="content">
<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.
</p>
<video id="dollyzoom" autoplay controls muted loop playsinline height="100%">
<source src="./static/videos/dollyzoom-stacked.mp4"
type="video/mp4">
</video>
</div>
</div>
<!--/ Visual Effects. -->
<!-- Matting. -->
<div class="column">
<h2 class="title is-3">Matting</h2>
<div class="columns is-centered">
<div class="column content">
<p>
As a byproduct of our method, we can also solve the matting problem by ignoring
samples that fall outside of a bounding box during rendering.
</p>
<video id="matting-video" controls playsinline height="100%">
<source src="./static/videos/matting.mp4"
type="video/mp4">
</video>
</div>
</div>
</div>
</div>
<!--/ Matting. -->
<!-- Animation. -->
<div class="columns is-centered">
<div class="column is-full-width">
<h2 class="title is-3">Animation</h2>
<!-- Interpolating. -->
<h3 class="title is-4">Interpolating states</h3>
<div class="content has-text-justified">
<p>
We can also animate the scene by interpolating the deformation latent codes of two input
frames. Use the slider here to linearly interpolate between the left frame and the right
frame.
</p>
</div>
<div class="columns is-vcentered interpolation-panel">
<div class="column is-3 has-text-centered">
<img src="./static/images/interpolate_start.jpg"
class="interpolation-image"
alt="Interpolate start reference image."/>
<p>Start Frame</p>
</div>
<div class="column interpolation-video-column">
<div id="interpolation-image-wrapper">
Loading...
</div>
<input class="slider is-fullwidth is-large is-info"
id="interpolation-slider"
step="1" min="0" max="100" value="0" type="range">
</div>
<div class="column is-3 has-text-centered">
<img src="./static/images/interpolate_end.jpg"
class="interpolation-image"
alt="Interpolation end reference image."/>
<p class="is-bold">End Frame</p>
</div>
</div>
<br/>
<!--/ Interpolating. -->
<!-- Re-rendering. -->
<h3 class="title is-4">Re-rendering the input video</h3>
<div class="content has-text-justified">
<p>
Using <span class="dnerf">Nerfies</span>, you can re-render a video from a novel
viewpoint such as a stabilized camera by playing back the training deformations.
</p>
</div>
<div class="content has-text-centered">
<video id="replay-video"
controls
muted
preload
playsinline
width="75%">
<source src="./static/videos/replay.mp4"
type="video/mp4">
</video>
</div>
<!--/ Re-rendering. -->
</div>
</div>
<!--/ Animation. -->
</div>
</section>
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<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>
</div>
</section>
<footer class="footer">
<div class="container">
<div class="columns is-centered">
<div class="column">
<div class="content has-text-centered">
<p>
Website template borrowed from
<a href="https://github.com/nerfies/nerfies.github.io">NeRFies</a>
made by the amazing <a href="https://keunhong.com/">Keunhong Park</a>.
</p>
</div>
</div>
</div>
</div>
</footer>
</body>
</html>