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<!DOCTYPE HTML>
<html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<!-- Hi, Jon Here. Please DELETE the two <script> tags below if you use this HTML, otherwise my analytics will track your page -->
<!-- Global site tag (gtag.js) - Google Analytics -->
<script async src="https://www.googletagmanager.com/gtag/js?id=UA-7580334-2"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'UA-7580334-2');
</script>
<title>Jon Barron</title>
<meta name="author" content="Jon Barron">
<meta name="viewport" content="width=device-width, initial-scale=1">
<link rel="stylesheet" type="text/css" href="stylesheet.css">
<link rel="icon" type="image/png" href="images/seal_icon.png">
</head>
<body>
<table style="width:100%;max-width:800px;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<tr style="padding:0px">
<td style="padding:0px">
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<tr style="padding:0px">
<td style="padding:2.5%;width:63%;vertical-align:middle">
<p style="text-align:center">
<name>Jon Barron</name>
</p>
<p>I am a staff research scientist at <a href="https://ai.google/research">Google Research</a>, where I work on computer vision and machine learning.
</p>
<p>
At Google I've worked on <a href="https://ai.googleblog.com/2014/04/lens-blur-in-new-google-camera-app.html">Lens Blur</a>, <a href="https://ai.googleblog.com/2014/10/hdr-low-light-and-high-dynamic-range.html">HDR+</a>, <a href="https://www.google.com/get/cardboard/jump/">Jump</a>, <a href="https://ai.googleblog.com/2017/10/portrait-mode-on-pixel-2-and-pixel-2-xl.html">Portrait Mode</a>, and <a href="https://www.youtube.com/watch?v=JSnB06um5r4">Glass</a>. I did my PhD at <a href="http://www.eecs.berkeley.edu/">UC Berkeley</a>, where I was advised by <a href="http://www.cs.berkeley.edu/~malik/">Jitendra Malik</a> and funded by the <a href="http://www.nsfgrfp.org/">NSF GRFP</a>. I did my bachelors at the <a href="http://cs.toronto.edu">University of Toronto</a>.
I've recieved the <a href="https://www2.eecs.berkeley.edu/Students/Awards/15/">C.V. Ramamoorthy Distinguished Research Award</a> and the <a href="https://www.thecvf.com/?page_id=413#YRA">PAMI Young Researcher Award</a>.
</p>
<p style="text-align:center">
<a href="mailto:jonbarron@gmail.com">Email</a>  / 
<a href="data/JonBarron-CV.pdf">CV</a>  / 
<a href="data/JonBarron-bio.txt">Biography</a>  / 
<a href="https://scholar.google.com/citations?hl=en&user=jktWnL8AAAAJ">Google Scholar</a>  / 
<a href="https://twitter.com/jon_barron">Twitter</a>
</p>
</td>
<td style="padding:2.5%;width:40%;max-width:40%">
<a href="images/JonBarron.jpg"><img style="width:100%;max-width:100%" alt="profile photo" src="images/JonBarron_circle.jpg" class="hoverZoomLink"></a>
</td>
</tr>
</tbody></table>
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<tr>
<td style="padding:20px;width:100%;vertical-align:middle">
<heading>Research</heading>
<p>
I'm interested in computer vision, machine learning, optimization, and image processing.
Much of my research is about inferring the physical world (shape, motion, color, light, etc) from images.
Representative papers are <span class="highlight">highlighted</span>.
</p>
</td>
</tr>
</tbody></table>
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<tr onmouseout="nlt_stop()" onmouseover="nlt_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='nlt_image'><video width=100% height=100% muted autoplay loop>
<source src="images/nlt_after.mp4" type="video/mp4">
Your browser does not support the video tag.
</video></div>
<img src='images/nlt_before.jpg' alt="Without effect" width="160">
</div>
<script type="text/javascript">
function nlt_start() {
document.getElementById('nlt_image').style.opacity = "1";
}
function nlt_stop() {
document.getElementById('nlt_image').style.opacity = "0";
}
nlt_stop()
</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="http://nlt.csail.mit.edu/">
<papertitle>Neural Light Transport for Relighting and View Synthesis</papertitle>
</a>
<br>
<a href="http://people.csail.mit.edu/xiuming/">Xiuming Zhang</a>,
<a href="http://www.seanfanello.it/">Sean Fanello</a>,
<a href="https://research.google/people/105312/">Yun-Ta Tsai</a>,
<a href="http://kevinkingo.com/">Tiancheng Sun</a>,
<a href="https://people.csail.mit.edu/tfxue/">Tianfan Xue</a>,
<a href="https://research.google/people/106687/">Rohit Pandey</a>,
<a href="https://www.dtic.ua.es/~sorts/">Sergio Orts-Escolano</a>,
<a href="https://dl.acm.org/profile/99659224296">Philip Davidson</a>,
<a href="https://scholar.google.com/citations?user=5D0_pjcAAAAJ&hl=en">Christoph Rhemann</a>,
<a href="http://www.pauldebevec.com/">Paul Debevec</a>,
<strong>Jonathan T. Barron</strong>,
<a href="http://cseweb.ucsd.edu/~ravir/">Ravi Ramamoorthi</a>,
<a href="http://billf.mit.edu/">William T. Freeman</a>
<br>
<em>arXiv</em>, 2020
<br>
<a href="http://nlt.csail.mit.edu/">project page</a> /
<a href="https://arxiv.org/abs/2008.03806">arXiv</a> /
<a href="https://www.youtube.com/watch?v=OGEnCWZihHE">video</a>
<p></p>
<p>Embedding a convnet within a predefined texture atlas enables simultaneous view synthesis and relighting.</p>
</td>
</tr>
<tr onmouseout="nerfw_stop()" onmouseover="nerfw_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='nerfw_image'><video width=100% height=100% muted autoplay loop>
<source src="images/nerfw_after.mp4" type="video/mp4">
Your browser does not support the video tag.
</video></div>
<img src='images/nerfw_before.jpg' width="160">
</div>
<script type="text/javascript">
function nerfw_start() {
document.getElementById('nerfw_image').style.opacity = "1";
}
function nerfw_stop() {
document.getElementById('nerfw_image').style.opacity = "0";
}
nerfw_stop()
</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://nerf-w.github.io/">
<papertitle>NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections</papertitle>
</a>
<br>
<a href="http://www.ricardomartinbrualla.com/">Ricardo Martin-Brualla*</a>,
<a href="https://scholar.google.com/citations?user=g98QcZUAAAAJ&hl=en">Noha Radwan*</a>,
<a href="https://research.google/people/105804/">Mehdi S. M. Sajjadi*</a>,
<strong>Jonathan T. Barron</strong>,
<a href="https://scholar.google.com/citations?user=FXNJRDoAAAAJ&hl=en">Alexey Dosovitskiy</a>,
<a href="http://www.stronglyconvex.com/about.html">Daniel Duckworth</a>
<br>
<em>arXiv</em>, 2020
<br>
<a href="https://nerf-w.github.io/">project page</a> /
<a href="https://arxiv.org/abs/2008.02268">arXiv</a> /
<a href="https://www.youtube.com/watch?v=yPKIxoN2Vf0">video</a>
<p></p>
<p>Letting NeRF reason about occluders and appearance variation produces photorealistic view synthesis using only unstructured internet photos.</p>
</td>
</tr>
<tr onmouseout="ff_stop()" onmouseover="ff_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='ff_image'>
<img src='images/lion_ff.jpg' width="160"></div>
<img src='images/lion_none.jpg' width="160">
</div>
<script type="text/javascript">
function ff_start() {
document.getElementById('ff_image').style.opacity = "1";
}
function ff_stop() {
document.getElementById('ff_image').style.opacity = "0";
}
ff_stop()
</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://people.eecs.berkeley.edu/~bmild/fourfeat/index.html">
<papertitle>Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains</papertitle>
</a>
<br>
<a href="http://matthewtancik.com/">Matthew Tancik*</a>,
<a href="https://people.eecs.berkeley.edu/~pratul/">Pratul Srinivasan*</a>,
<a href="https://people.eecs.berkeley.edu/~bmild/">Ben Mildenhall*</a>,
<a href="https://people.eecs.berkeley.edu/~sfk/">Sara Fridovich-Keil</a>,
<a href="https://www.linkedin.com/in/nithinraghavan">Nithin Raghavan</a>,
<a href="https://scholar.google.com/citations?user=lvA86MYAAAAJ&hl=en">Utkarsh Singhal</a>,
<a href="http://cseweb.ucsd.edu/~ravir/">Ravi Ramamoorthi</a>,
<strong>Jonathan T. Barron</strong>,
<a href="https://www2.eecs.berkeley.edu/Faculty/Homepages/yirenng.html">Ren Ng</a>
<br>
<em>NeurIPS</em>, 2020   <font color=#FF8080><strong>(Spotlight)</strong></font>
<br>
<a href="https://people.eecs.berkeley.edu/~bmild/fourfeat/">project page</a> /
<a href="https://arxiv.org/abs/2006.10739">arXiv</a> /
<a href="https://github.com/tancik/fourier-feature-networks">code</a>
<p></p>
<p>Composing neural networks with a simple Fourier feature mapping allows them to learn detailed high-frequency functions.</p>
</td>
</tr>
<tr onmouseout="thresh_stop()" onmouseover="thresh_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='thresh_image'>
<img src='images/thresh_after.png' width="160"></div>
<img src='images/thresh_before.jpg' width="160">
</div>
<script type="text/javascript">
function thresh_start() {
document.getElementById('thresh_image').style.opacity = "1";
}
function thresh_stop() {
document.getElementById('thresh_image').style.opacity = "0";
}
thresh_stop()
</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://arxiv.org/abs/2007.07350">
<papertitle>A Generalization of Otsu's Method and Minimum Error Thresholding</papertitle>
</a>
<br>
<strong>Jonathan T. Barron</strong>
<br>
<em>ECCV</em>, 2020   <font color=#FF8080><strong>(Spotlight)</strong></font>
<br>
<a href="https://github.com/jonbarron/hist_thresh">code</a> /
<a href="https://www.youtube.com/watch?v=rHtQQlQo1Q4">video</a> /
<a href="data/BarronECCV2020.bib">bibtex</a>
<br>
<p></p>
<p>
A simple and fast Bayesian algorithm that can be written in ~10 lines of code outperforms or matches giant CNNs on image binarization, and unifies three classic thresholding algorithms.
</p>
</td>
</tr>
<tr onmouseout="uflow_stop()" onmouseover="uflow_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='uflow_image'>
<img src='images/uflow_after.png' width="160"></div>
<img src='images/uflow_before.jpg' width="160">
</div>
<script type="text/javascript">
function uflow_start() {
document.getElementById('uflow_image').style.opacity = "1";
}
function uflow_stop() {
document.getElementById('uflow_image').style.opacity = "0";
}
uflow_stop()
</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://arxiv.org/abs/2006.04902">
<papertitle>What Matters in Unsupervised Optical Flow</papertitle>
</a>
<br>
<a href="http://ricojonschkowski.com/">Rico Jonschkowski</a>,
<a href="https://www.linkedin.com/in/austin-charles-stone-1ba33b138/">Austin Stone</a>,
<strong>Jonathan T. Barron</strong>,
<a href="https://research.google/people/ArielGordon/">Ariel Gordon</a>,
<a href="https://www.linkedin.com/in/kurt-konolige/">Kurt Konolige</a>,
<a href="https://research.google/people/AneliaAngelova/">Anelia Angelova</a>
<br>
<em>ECCV</em>, 2020   <font color="red"><strong>(Oral Presentation)</strong></font>
<br>
<a href="https://github.com/google-research/google-research/tree/master/uflow">code</a>
<br>
<p></p>
<p>
Extensive experimentation yields a simple optical flow technique that is trained on only unlabeled videos, but still works as well as supervised techniques.
</p>
</td>
</tr>
<tr onmouseout="nerf_stop()" onmouseover="nerf_start()" bgcolor="#ffffd0">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='nerf_image'><video width=100% height=100% muted autoplay loop>
<source src="images/vase_small.mp4" type="video/mp4">
Your browser does not support the video tag.
</video></div>
<img src='images/vase_still.png' width="160">
</div>
<script type="text/javascript">
function nerf_start() {
document.getElementById('nerf_image').style.opacity = "1";
}
function nerf_stop() {
document.getElementById('nerf_image').style.opacity = "0";
}
nerf_stop()
</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="http://www.matthewtancik.com/nerf">
<papertitle>NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis</papertitle>
</a>
<br>
<a href="https://people.eecs.berkeley.edu/~bmild/">Ben Mildenhall*</a>,
<a href="https://people.eecs.berkeley.edu/~pratul/">Pratul Srinivasan*</a>,
<a href="http://matthewtancik.com/">Matthew Tancik*</a>,
<strong>Jonathan T. Barron</strong>,
<a href="http://cseweb.ucsd.edu/~ravir/">Ravi Ramamoorthi</a>,
<a href="https://www2.eecs.berkeley.edu/Faculty/Homepages/yirenng.html">Ren Ng</a>
<br>
<em>ECCV</em>, 2020   <font color="red"><strong>(Oral Presentation, Best Paper Honorable Mention)</strong></font>
<br>
<a href="http://www.matthewtancik.com/nerf">project page</a>
/
<a href="https://arxiv.org/abs/2003.08934">arXiv</a>
/
<a href="https://www.youtube.com/watch?v=LRAqeM8EjOo&t">talk video</a>
/
<a href="https://www.youtube.com/watch?v=JuH79E8rdKc">supp video</a>
/
<a href="https://github.com/bmild/nerf">code</a>
<p></p>
<p>
Training a tiny non-convolutional neural network to reproduce a scene using volume rendering achieves photorealistic view synthesis.</p>
</td>
</tr>
<tr onmouseout="porshadmanip_stop()" onmouseover="porshadmanip_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='porshadmanip_image'>
<img src='images/porshadmanip_after.jpg' width="160"></div>
<img src='images/porshadmanip_before.jpg' width="160">
</div>
<script type="text/javascript">
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porshadmanip_stop()
</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://arxiv.org/abs/2005.08925">
<papertitle>Portrait Shadow Manipulation</papertitle>
</a>
<br>
<a href="https://people.eecs.berkeley.edu/~cecilia77/">Xuaner (Cecilia) Zhang</a>,
<strong>Jonathan T. Barron</strong>,
<a href="https://ai.google/research/people/105312/">Yun-Ta Tsai</a>,
<a href="https://www.linkedin.com/in/rohit-pandey-bab10b7a/">Rohit Pandey</a>,
<a href="http://people.csail.mit.edu/xiuming/">Xiuming Zhang</a>,
<a href="http://graphics.stanford.edu/~renng/">Ren Ng</a>,
<a href="http://graphics.stanford.edu/~dejacobs/">David E. Jacobs</a>
<br>
<em>SIGGRAPH</em>, 2020
<br>
<a href="https://people.eecs.berkeley.edu/~cecilia77/project-pages/portrait">project page</a> /
<a href="https://www.youtube.com/watch?v=M_qYTXhzyac">video</a>
<p></p>
<p>Networks can be trained to remove shadows cast on human faces and to soften harsh lighting.</p>
</td>
</tr>
<tr onmouseout="learnaf_stop()" onmouseover="learnaf_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='learnaf_image'>
<img src='images/learnaf_after.jpg' width="160"></div>
<img src='images/learnaf_before.jpg' width="160">
</div>
<script type="text/javascript">
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}
learnaf_stop()
</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://arxiv.org/abs/2004.12260">
<papertitle>Learning to Autofocus</papertitle>
</a>
<br>
<a href="">Charles Herrmann</a>,
<a href="">Richard Strong Bowen</a>,
<a href="http://nealwadhwa.com">Neal Wadhwa</a>,
<a href="http://rahuldotgarg.appspot.com/">Rahul Garg</a>,
<a href="https://scholar.google.com/citations?user=BxqV_RsAAAAJ">Qiurui He</a>,
<strong>Jonathan T. Barron</strong>,
<a href="http://www.cs.cornell.edu/~rdz/index.htm">Ramin Zabih</a>
<br>
<em>CVPR</em>, 2020
<br>
<a href="https://arxiv.org/abs/2004.12260">arXiv</a>
<p></p>
<p>Machine learning can be used to train cameras to autofocus (which is not the same problem as "depth from defocus").</p>
</td>
</tr>
<tr onmouseout="lighthouse_stop()" onmouseover="lighthouse_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='lh_image'><video width=100% height=100% muted autoplay loop>
<source src="images/rings_crop.mp4" type="video/mp4">
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</video></div>
<img src='images/rings.png' width="160">
</div>
<script type="text/javascript">
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lighthouse_stop()
</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://people.eecs.berkeley.edu/~pratul/lighthouse/">
<papertitle>Lighthouse: Predicting Lighting Volumes for Spatially-Coherent Illumination</papertitle>
</a>
<br>
<a href="https://people.eecs.berkeley.edu/~pratul/">Pratul Srinivasan*</a>,
<a href="https://people.eecs.berkeley.edu/~bmild/">Ben Mildenhall*</a>,
<a href="http://matthewtancik.com/">Matthew Tancik</a>,
<strong>Jonathan T. Barron</strong>,
<a href="https://research.google/people/RichardTucker/">Richard Tucker</a>,
<a href="https://www.cs.cornell.edu/~snavely/">Noah Snavely</a>
<br>
<em>CVPR</em>, 2020
<br>
<a href="https://people.eecs.berkeley.edu/~pratul/lighthouse/">project page</a>
/
<a href="https://github.com/pratulsrinivasan/lighthouse">code</a>
/
<a href="https://arxiv.org/abs/2003.08367">arXiv</a>
/
<a href="https://www.youtube.com/watch?v=KsiZpUFPqIU">video</a>
<p></p>
<p>We predict a volume from an input stereo pair that can be used to calculate incident lighting at any 3D point within a scene.</p>
</td>
</tr>
<tr onmouseout="skyopt_stop()" onmouseover="skyopt_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='skyopt_image'>
<img src='images/skyopt_after.jpg' width="160"></div>
<img src='images/skyopt_before.jpg' width="160">
</div>
<script type="text/javascript">
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</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://arxiv.org/abs/2006.10172">
<papertitle>Sky Optimization: Semantically Aware Image Processing of Skies in Low-Light Photography</papertitle>
</a>
<br>
<a href="https://sites.google.com/corp/view/orly-liba/">Orly Liba</a>,
<a href="https://www.linkedin.com/in/longqicai/en-us">Longqi Cai</a>,
<a href="https://ai.google/research/people/105312/">Yun-Ta Tsai</a>,
<a href="https://research.google/people/EladEban/">Elad Eban</a>,
<a href="https://research.google/people/YairMovshovitzAttias/">Yair Movshovitz-Attias</a>,
<a href="https://scholar.google.com/citations?user=2jXxOYQAAAAJ">Yael Pritch</a>,
<a href="https://www.linkedin.com/in/huizhong-chen-00776432">Huizhong Chen</a>,
<strong>Jonathan T. Barron</strong>
<br>
<em>NTIRE CVPRW</em>, 2020
<br>
<a href="https://google.github.io/sky-optimization/">project page</a>
<p></p>
<p>If you want to photograph the sky, it helps to know where the sky is.</p>
</td>
</tr>
<tr onmouseout="nightsight_stop()" onmouseover="nightsight_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='nightsight_image'><img src='images/nightsight_after.jpg'></div>
<img src='images/nightsight_before.jpg'>
</div>
<script type="text/javascript">
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</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://arxiv.org/abs/1910.11336">
<papertitle>Handheld Mobile Photography in Very Low Light</papertitle>
</a>
<br>
<a href="https://sites.google.com/site/orlylibaprofessional/">Orly Liba</a>,
<a href="https://scholar.google.com/citations?user=6PhlPWMAAAAJ">Kiran Murthy</a>,
<a href="https://ai.google/research/people/105312/">Yun-Ta Tsai</a>,
<a href="https://www.timothybrooks.com/">Timothy Brooks</a>,
<a href="https://people.csail.mit.edu/tfxue/">Tianfan Xue</a>,
<a href="https://scholar.google.com/citations?user=qgc_jY0AAAAJ">Nikhil Karnad</a>,
<a href="https://scholar.google.com/citations?user=BxqV_RsAAAAJ">Qiurui He</a>,
<strong>Jonathan T. Barron</strong>,
<a href="https://ai.google/research/people/105641/">Dillon Sharlet</a>,
<a href="http://www.geisswerks.com/">Ryan Geiss</a>,
<a href="https://people.csail.mit.edu/hasinoff/">Samuel W. Hasinoff</a>,
<a href="https://scholar.google.com/citations?user=2jXxOYQAAAAJ">Yael Pritch</a>,
<a href="http://graphics.stanford.edu/~levoy/">Marc Levoy</a>
<br>
<em>SIGGRAPH Asia</em>, 2019
<br>
<a href="https://github.com/google/night-sight/tree/master/docs">project page</a>
<br>
<p></p>
<p>By rethinking metering, white balance, and tone mapping, we can take pictures in places too dark for humans to see clearly.</p>
</td>
</tr>
<tr onmouseout="font_stop()" onmouseover="font_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='font_image'><img src='images/font_after.png'></div>
<img src='images/font_before.png'>
</div>
<script type="text/javascript">
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</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://arxiv.org/abs/1910.00748">
<papertitle>A Deep Factorization of Style and Structure in Fonts</papertitle>
</a>
<br>
<a href="http://www.cs.cmu.edu/~asrivats/">Nikita Srivatsan</a>,
<strong>Jonathan T. Barron</strong>,
<a href="https://people.eecs.berkeley.edu/~klein/">Dan Klein</a>,
<a href="http://cseweb.ucsd.edu/~tberg/">Taylor Berg-Kirkpatrick</a>
<br>
<em>EMNLP</em>, 2019   <font color="red"><strong>(Oral Presentation)</strong></font>
<br>
<p></p>
<p>Variational auto-encoders can be used to disentangle a characters style from its content.</p>
</td>
</tr>
<tr onmouseout="dpzlearn_stop()" onmouseover="dpzlearn_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='dpzlearn_image'><img src='images/dpzlearn_after.jpg'></div>
<img src='images/dpzlearn_before.jpg'>
</div>
<script type="text/javascript">
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</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://arxiv.org/abs/1904.05822">
<papertitle>Learning Single Camera Depth Estimation using Dual-Pixels</papertitle>
</a>
<br>
<a href="http://rahuldotgarg.appspot.com/">Rahul Garg</a>,
<a href="http://nealwadhwa.com">Neal Wadhwa</a>,
<a href="">Sameer Ansari,</a>,
<strong>Jonathan T. Barron</strong>
<br>
<em>ICCV</em>, 2019   <font color="red"><strong>(Oral Presentation)</strong></font>
<br>
<a href="https://github.com/google-research/google-research/tree/master/dual_pixels">code</a> /
<a href="data/GargICCV2019.bib">bibtex</a>
<p></p>
<p>Considering the optics of dual-pixel image sensors improves monocular depth estimation techniques.</p>
</td>
</tr>
<tr onmouseout="porlight_stop()" onmouseover="porlight_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='porlight_image'><img src='images/porlight_after.jpg'></div>
<img src='images/porlight_before.jpg'>
</div>
<script type="text/javascript">
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</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://arxiv.org/abs/1905.00824">
<papertitle>Single Image Portrait Relighting</papertitle>
</a>
<br>
<a href="http://kevinkingo.com/">Tiancheng Sun</a>,
<strong>Jonathan T. Barron</strong>,
<a href="https://ai.google/research/people/105312/">Yun-Ta Tsai</a>,
<a href="https://cseweb.ucsd.edu/~zex014/">Zexiang Xu</a>, Xueming Yu,
<a href="http://ict.usc.edu/profile/graham-fyffe/">Graham Fyffe</a>, Christoph Rhemann, Jay Busch,
<a href="https://www.pauldebevec.com/">Paul Debevec</a>,
<a href="https://cseweb.ucsd.edu/~ravir/">Ravi Ramamoorthi</a>
<br>
<em>SIGGRAPH</em>, 2019
<br>
<a href="https://www.youtube.com/watch?v=yxhGWds_g4I">video</a> /
<a href="https://petapixel.com/2019/07/16/researchers-developed-an-ai-that-can-relight-portraits-after-the-fact/">press</a> /
<a href="data/SunSIGGRAPH2019.bib">bibtex</a>
<br>
<p></p>
<p>Training a neural network on light stage scans and environment maps produces an effective relighting method.</p>
</td>
</tr>
<tr onmouseout="loss_stop()" onmouseover="loss_start()" bgcolor="#ffffd0">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='loss_image'><img src='images/loss_after.png'></div>
<img src='images/loss_before.png'>
</div>
<script type="text/javascript">
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</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://drive.google.com/open?id=1xpZ0fL9h1y9RfcTyPgVkxUrF3VwdkBvq">
<papertitle>A General and Adaptive Robust Loss Function</papertitle>
</a>
<br>
<strong>Jonathan T. Barron</strong>
<br>
<em>CVPR</em>, 2019   <font color="red"><strong>(Oral Presentation, Best Paper Award Finalist)</strong></font>
<br>
<a href="https://arxiv.org/abs/1701.03077">arxiv</a> /
<a href="https://drive.google.com/open?id=1HNveL7xSNh6Ss7sxLK8Mw2L1Fc-rRhL4">supplement</a> /
<a href="https://youtu.be/BmNKbnF69eY">video</a> /
<a href="https://www.youtube.com/watch?v=4IInDT_S0ow&t=37m22s">talk</a> /
<a href="https://drive.google.com/file/d/1GzRYRIfLHvNLT_QwjHoBjHkBbs3Nbf0x/view?usp=sharing">slides</a> /
<a href="https://github.com/google-research/google-research/tree/master/robust_loss">tensorflow code</a> /
<a href="https://github.com/jonbarron/robust_loss_pytorch">pytorch code</a> /
<a href="data/BarronCVPR2019_reviews.txt">reviews</a> /
<a href="data/BarronCVPR2019.bib">bibtex</a>
<p></p>
<p>A single robust loss function is a superset of many other common robust loss functions, and allows training to automatically adapt the robustness of its own loss.</p>
</td>
</tr>
<tr onmouseout="mpi_stop()" onmouseover="mpi_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='mpi_image'><img src='images/mpi_after.jpg'></div>
<img src='images/mpi_before.jpg'>
</div>
<script type="text/javascript">
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</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://drive.google.com/file/d/1TU5L6fnt4Kd49IUOU7aNxor5NIgdHuNG/view?usp=sharing">
<papertitle>Pushing the Boundaries of View Extrapolation with Multiplane Images</papertitle>
</a>
<br>
<a href="https://people.eecs.berkeley.edu/~pratul/">Pratul P. Srinivasan</a>, Richard Tucker,
<strong>Jonathan T. Barron</strong>,
<a href="http://cseweb.ucsd.edu/~ravir/">Ravi Ramamoorthi</a>,
<a href="http://graphics.stanford.edu/~renng/">Ren Ng</a>,
<a href="https://www.cs.cornell.edu/~snavely/">Noah Snavely</a>
<br>
<em>CVPR</em>, 2019   <font color="red"><strong>(Oral Presentation, Best Paper Award Finalist)</strong></font>
<br>
<a href="https://drive.google.com/file/d/1GUW_n-BAn9Q4VntEA_OTHNJiHO7XfC62/view?usp=sharing">supplement</a> /
<a href="https://www.youtube.com/watch?v=aJqAaMNL2m4">video</a> /
<a href="data/SrinivasanCVPR2019.bib">bibtex</a>
<p></p>
<p>View extrapolation with multiplane images works better if you reason about disocclusions and disparity sampling frequencies.</p>
</td>
</tr>
<tr onmouseout="unprocessing_stop()" onmouseover="unprocessing_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='unprocessing_image'><img src='images/unprocessing_after.jpg'></div>
<img src='images/unprocessing_before.jpg'>
</div>
<script type="text/javascript">
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</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://drive.google.com/file/d/1H0Wtd--un2JN76dUJN8iC9fWfkA16n8D/view?usp=sharing">
<papertitle>Unprocessing Images for Learned Raw Denoising</papertitle>
</a>
<br>
<a href="http://timothybrooks.com/">Tim Brooks</a>,
<a href="https://people.eecs.berkeley.edu/~bmild/">Ben Mildenhall</a>,
<a href="https://people.csail.mit.edu/tfxue/">Tianfan Xue</a>,
<a href="http://people.csail.mit.edu/jiawen/">Jiawen Chen</a>,
<a href="http://www.dsharlet.com/">Dillon Sharlet</a>,
<strong>Jonathan T. Barron</strong>
<br>
<em>CVPR</em>, 2019   <font color="red"><strong>(Oral Presentation)</strong></font>
<br>
<a href="https://arxiv.org/abs/1811.11127">arxiv</a> /
<a href="http://timothybrooks.com/tech/unprocessing/">project page</a> /
<a href="https://github.com/google-research/google-research/tree/master/unprocessing">code</a> /
<a href="data/BrooksCVPR2019.bib">bibtex</a>
<p></p>
<p>We can learn a better denoising model by processing and unprocessing images the same way a camera does.</p>
</td>
</tr>
<tr onmouseout="motionblur_stop()" onmouseover="motionblur_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='motionblur_image'><img src='images/motionblur_after.jpg'></div>
<img src='images/motionblur_before.jpg'>
</div>
<script type="text/javascript">
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</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://drive.google.com/file/d/1hWpA4f6iLVcOkZI3zEAAWKARSQhnVgbY/view?usp=sharing">
<papertitle>Learning to Synthesize Motion Blur</papertitle>
</a>
<br>
<a href="http://timothybrooks.com/">Tim Brooks</a>,
<strong>Jonathan T. Barron</strong>
<br>
<em>CVPR</em>, 2019   <font color="red"><strong>(Oral Presentation)</strong></font>
<br>
<a href="https://arxiv.org/abs/1811.11745">arxiv</a> /
<a href="https://drive.google.com/file/d/1dUQwBMmQdYYIP0zHR_nDQY-uQbaMdcSN/view?usp=sharing">supplement</a> /
<a href="http://timothybrooks.com/tech/motion-blur/">project page</a> /
<a href="https://www.youtube.com/watch?v=8T1jjSz-2V8">video</a> /
<a href="https://github.com/google-research/google-research/tree/master/motion_blur">code</a> /
<a href="data/BrooksBarronCVPR2019.bib">bibtex</a>
<p></p>
<p>Frame interpolation techniques can be used to train a network that directly synthesizes linear blur kernels.</p>
</td>
</tr>
<tr onmouseout="darkflash_stop()" onmouseover="darkflash_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='darkflash_image'><img src='images/darkflash_after.png'></div>
<img src='images/darkflash_before.png'>
</div>
<script type="text/javascript">
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</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://arxiv.org/abs/1901.01370">
<papertitle>Stereoscopic Dark Flash for Low-light Photography</papertitle>
</a>
<br>
<a href="https://www.andrew.cmu.edu/user/jianwan2/">Jian Wang</a>,
<a href="https://people.csail.mit.edu/tfxue/">Tianfan Xue</a>,
<strong>Jonathan T. Barron</strong>,
<a href="http://people.csail.mit.edu/jiawen/">Jiawen Chen</a>
<br>
<em>ICCP</em>, 2019
<br>
<p></p>
<p>
By making one camera in a stereo pair hyperspectral we can multiplex dark flash pairs in space instead of time.
</p>
</td>
</tr>
<tr onmouseout="motionstereo_stop()" onmouseover="motionstereo_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='motionstereo_image'><img src='images/motionstereo_after.png'></div>
<img src='images/motionstereo_before.png'>
</div>
<script type="text/javascript">
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<a href="https://drive.google.com/file/d/1AABFJ3NgD5DAo5JEpEjWZrcQNzjZnvW9/view?usp=sharing">
<papertitle>Depth from Motion for Smartphone AR</papertitle>
</a>
<br>
<a href="https://www.linkedin.com/in/valentinjulien/">Julien Valentin</a>,
<a href="https://www.linkedin.com/in/adarshkowdle/">Adarsh Kowdle</a>,
<strong>Jonathan T. Barron</strong>, <a href="http://nealwadhwa.com">Neal Wadhwa</a>, and others
<br>
<em>SIGGRAPH Asia</em>, 2018
<br>
<a href="data/Valentin2018.bib">bibtex</a>
<p></p>
<p>Depth cues from camera motion allow for real-time occlusion effects in augmented reality applications.</p>
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<tr onmouseout="portrait_stop()" onmouseover="portrait_start()">
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<div class="one">
<div class="two" id='portrait_image'><img src='images/portrait_after.jpg'></div>
<img src='images/portrait_before.jpg'>
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<script type="text/javascript">
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<a href="https://drive.google.com/file/d/13i6DlS9UhGVKmwslLUFnKBwdxFRVQeQj/view?usp=sharing">
<papertitle>Synthetic Depth-of-Field with a Single-Camera Mobile Phone</papertitle>
</a>
<br>
<a href="http://nealwadhwa.com">Neal Wadhwa</a>,
<a href="http://rahuldotgarg.appspot.com/">Rahul Garg</a>,
<a href="http://graphics.stanford.edu/~dejacobs/">David E. Jacobs</a>, Bryan E. Feldman, Nori Kanazawa, Robert Carroll,
<a href="http://www.cs.cmu.edu/~ymovshov/">Yair Movshovitz-Attias</a>,
<strong>Jonathan T. Barron</strong>, Yael Pritch,
<a href="http://graphics.stanford.edu/~levoy/">Marc Levoy</a>
<br>
<em>SIGGRAPH</em>, 2018
<br>
<a href="https://arxiv.org/abs/1806.04171">arxiv</a> /
<a href="https://ai.googleblog.com/2017/10/portrait-mode-on-pixel-2-and-pixel-2-xl.html">blog post</a> /
<a href="data/Wadhwa2018.bib">bibtex</a>
<p></p>
<p>Dual pixel cameras and semantic segmentation algorithms can be used for shallow depth of field effects.</p>
<p>This system is the basis for "Portrait Mode" on the Google Pixel 2 smartphones</p>
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<a href="https://drive.google.com/file/d/1MpvxcW7OTJP321QL_q4ZLQ8D653bZZzy/view?usp=sharing">
<papertitle>Aperture Supervision for Monocular Depth Estimation</papertitle>
</a>
<br>
<a href="https://people.eecs.berkeley.edu/~pratul/">Pratul P. Srinivasan</a>,
<a href="http://rahuldotgarg.appspot.com/">Rahul Garg</a>,
<a href="http://nealwadhwa.com">Neal Wadhwa</a>,
<a href="http://graphics.stanford.edu/~renng/">Ren Ng</a>,
<strong>Jonathan T. Barron</strong>
<br>
<em>CVPR</em>, 2018
<br>
<a href="https://github.com/google/aperture_supervision">code</a> /
<a href="data/Srinivasan2018.bib">bibtex</a>
<p></p>
<p>Varying a camera's aperture provides a supervisory signal that can teach a neural network to do monocular depth estimation.</p>
</td>
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<a href="https://drive.google.com/file/d/1GAH8ijyZ7GnoBnQFANEzdXinHrE4vvXn/view?usp=sharing">
<papertitle>Burst Denoising with Kernel Prediction Networks</papertitle>
</a>
<br>
<a href="https://people.eecs.berkeley.edu/~bmild/">Ben Mildenhall</a>,
<strong>Jonathan T. Barron</strong>,
<a href="http://people.csail.mit.edu/jiawen/">Jiawen Chen</a>,
<a href="http://www.dsharlet.com/">Dillon Sharlet</a>,
<a href="http://graphics.stanford.edu/~renng/">Ren Ng</a>, Robert Carroll
<br>
<em>CVPR</em>, 2018   <font color=#FF8080><strong>(Spotlight)</strong></font>
<br>
<a href="https://drive.google.com/file/d/1aqk3Q-L2spjLZh2yRWKUWIDcZkGjQ7US/view?usp=sharing">supplement</a> /
<a href="https://github.com/google/burst-denoising">code</a> /
<a href="data/Mildenhall2018.bib">bibtex</a>
<p></p>
<p>We train a network to predict linear kernels that denoise noisy bursts from cellphone cameras.</p>
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