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<!DOCTYPE HTML>
<html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<title>Xiaoyu Shi</title>
<meta name="author" content="Xiaoyu Shi">
<meta name="viewport" content="width=device-width, initial-scale=1">
<link rel="stylesheet" type="text/css" href="stylesheet.css">
<link rel="icon" href="data:image/svg+xml,<svg xmlns=%22http://www.w3.org/2000/svg%22 viewBox=%220 0 100 100%22><text y=%22.9em%22 font-size=%2290%22>🌐</text></svg>">
</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>Xiaoyu Shi 石晓宇</name>
</p>
<p>I am a 4th-year Ph.D student at <a href="https://mmlab.ie.cuhk.edu.hk/">MMLab</a> of the Chinese University of Hong Kong, advised by <a href="https://www.ee.cuhk.edu.hk/~hsli/">Prof. Hongsheng Li</a>. Prior to this, I obtained Bachelor of Engineer & Bachelor of Science degrees from Zhejiang University and Simon Fraser University, respectively. I'm interested in computer vision and machine learning, with a special focus on video generation and correspondence learning.
</p>
<p style="text-align:center">
<a href="mailto:[email protected]">Email</a>  / 
<!-- <a href="data/JonBarron-CV.pdf">CV</a>  /  -->
<a href="https://scholar.google.com/citations?hl=en&user=fbEuTJUAAAAJ">Google Scholar</a>  / 
<a href="https://github.com/XiaoyuShi97/">Github</a>
</p>
</td>
<td style="padding:2.5%;width:40%;max-width:40%">
<a href="images/sxy_square.png"><img style="width:100%;max-width:100%" alt="profile photo" src="images/sxy_square.png" 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>News</heading>
<ul>
<li>Three papers accepted to ECCV 2024.</li>
<li>One paper accepted to SIGGRAPH 2024.</li>
<li>Our <a href="https://arxiv.org/abs/2303.08340">VideoFlow</a>, <a href="https://openaccess.thecvf.com/content/CVPR2023/html/Shi_FlowFormer_Masked_Cost_Volume_Autoencoding_for_Pretraining_Optical_Flow_Estimation_CVPR_2023_paper.html">FlowFormer++</a>, and <a href="https://drinkingcoder.github.io/publication/flowformer/">FlowFormer</a> occupy the TOP 3 places in the Sintel Optical Flow benchmark among published papers.</li>
<li>Two papers accepted to NeurIPS 2023.</li>
<li>One paper accepted to ICCV 2023.</li>
<li>One paper accepted to IROS 2023.</li>
<li>Two papers accepted to CVPR 2023.</li>
<li>One paper accepted to ECCV 2022.</li>
</ul>
</td>
</tr>
</tbody></table>
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<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>Publication</heading>
<p>
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="bakedsdf_stop()" onmouseover="bakedsdf_start()" bgcolor="#ffffd0">
<td style="padding:20px;width:25%;vertical-align:middle">
<video width=320 height=180 muted autoplay loop>
<source src="images/motioni2v-teaser.mp4" type="video/mp4" >
Your browser does not support the video tag.
</video>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<papertitle>Motion-I2V: Consistent and Controllable Image-to-Video Generation with Explicit Motion Modeling</papertitle>
<br>
<strong>Xiaoyu Shi</strong>,
Zhaoyang Huang,
Fu-Yun, Wang,
Weikang Bian,
Dasong Li,
Yi Zhang,
Manyuan Zhang,
Kachun Cheung,
Simon See,<br>
Hongwei Qin,
Jifeng Dai,
Hongsheng Li <br>
<em>Siggraph</em>, 2024
<br>
<a href="https://arxiv.org/abs/2401.15977">Paper</a>
/
<a href="https://xiaoyushi97.github.io/Motion-I2V/">Project page</a>
<p></p>
<p>
We introduce Motion-I2V, a novel framework for consistent and controllable image-to-video generation (I2V). In contrast to previous methods that directly learn the complicated image-to-video mapping, Motion-I2V factorizes I2V into two stages with explicit motion modeling.
</p>
</td>
</tr>
<tr onmouseout="bakedsdf_stop()" onmouseover="bakedsdf_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<video width=320 height=180 muted autoplay loop>
<source src="images/breakdance-flare_1_context-tap.mp4" type="video/mp4">
Your browser does not support the video tag.
</video>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<papertitle>Context-TAP: Tracking Any Point Demands Spatial Context Features</papertitle>
<br>
Weikang Bian*,
Zhaoyang Huang*,
<strong>Xiaoyu Shi</strong>,
Yitong Dong, <br>
Yijin Li,
Hongsheng Li <br>
<em>NeurIPS</em>, 2023
<br>
<a href="https://wkbian.github.io/Projects/Context-TAP/">Project page</a>
/
<a href="https://arxiv.org/abs/2306.02000">Paper</a>
<p></p>
<p>
We set new SOTA on the task of Tracking Any Point (TAP) by introducing rich spatial context features.
</p>
</td>
</tr>
<tr onmouseout="bakedsdf_stop()" onmouseover="bakedsdf_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<img src='images/unified_condition.png' width="320" height="180">
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<papertitle>A Unified Conditional Framework for Diffusion-based Image Restoration </papertitle>
<br>
Yi Zhang,
<strong>Xiaoyu Shi</strong>,
Dasong Li,
Xiaogang Wang,
Hongsheng Li <br>
<em>NeurIPS</em>, 2023
<br>
<a href="https://zhangyi-3.github.io/project/UCDIR">Project page</a>
/
<a href="https://arxiv.org/abs/2305.20049">Paper</a>
/
<a href="https://github.com/zhangyi-3/UCDIR">Code</a>
<p></p>
<p>
A unified conditional framework based on diffusion models for image restoration.
</p>
</td>
</tr>
<tr onmouseout="bakedsdf_stop()" onmouseover="bakedsdf_start()" bgcolor="#ffffd0">
<td style="padding:20px;width:25%;vertical-align:middle">
<video width=320 height=180 muted autoplay loop>
<source src="images/videoflow_demo.mp4" type="video/mp4" >
Your browser does not support the video tag.
</video>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<papertitle>VideoFlow: Exploiting Temporal Cues for Multi-frame Optical Flow Estimation</papertitle>
<br>
<strong>Xiaoyu Shi</strong>,
Zhaoyang Huang,
Weikang Bian,
Dasong Li, <br>
Manyuan Zhang,
Kachun Cheung,
Simon See,
Hongwei Qin, <br>
Jifeng Dai,
Hongsheng Li <br>
<em>ICCV</em>, 2023
<br>
<a href="https://arxiv.org/abs/2303.08340">Paper</a>
/
<a href="https://github.com/XiaoyuShi97/VideoFlow">Code</a>
<p></p>
<p>
First method to achieve sub-pixel accuracy on the Sintel benchmark. 19.2% error reduction from the best published result on the KITTI-2015 benchmark.
</p>
</td>
</tr>
<tr onmouseout="bakedsdf_stop()" onmouseover="bakedsdf_start()" bgcolor="#ffffd0">
<td style="padding:20px;width:25%;vertical-align:middle">
<video width=320 height=180 muted autoplay loop>
<source src="images/blinkflow.mp4" type="video/mp4" >
Your browser does not support the video tag.
</video>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<papertitle>BlinkFlow: A Dataset to Push the Limits of Event-based Optical Flow Estimation</papertitle>
<br>
Yijin Li,
Zhaoyang Huang,
Shuo Chen,
<strong>Xiaoyu Shi</strong>,
Hongsheng Li, <br>
Hujun Bao,
Zhaopeng Cui,
Guofeng Zhang <br>
<em>IROS</em>, 2023
<br>
<a href="https://arxiv.org/abs/2303.07716">Paper</a>
<p></p>
<p>
We build a benchmark BlinkFlow for training and evaluating event-based optical flow estimation method.
</p>
</td>
</tr>
<tr onmouseout="bakedsdf_stop()" onmouseover="bakedsdf_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<img src='images/kbnet.jpg' width="320" height="180">
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<papertitle>KBNet: Kernel Basis Network for Image Restoration</papertitle>
<br>
Yi Zhang,
Dasong Li,
<strong>Xiaoyu Shi</strong>,
Dailan He,
Kangning Song,
Xiaogang Wang,
Hongwei Qin,
Hongsheng Li <br>
<em>Arxiv</em>, 2023
<br>
<a href="https://arxiv.org/abs/2303.02881">Paper</a>
/
<a href="https://github.com/zhangyi-3/kbnet">Code</a>
<p></p>
<p>
A general-purpose backbone for image restoration tasks.
</p>
</td>
</tr>
<tr onmouseout="bakedsdf_stop()" onmouseover="bakedsdf_start()" bgcolor="#ffffd0">
<td style="padding:20px;width:25%;vertical-align:middle">
<video width=320 height=180 muted autoplay loop>
<source src="images/FF++_demo_small.mov" type="video/mp4" >
Your browser does not support the video tag.
</video>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<papertitle>FlowFormer++: Masked Cost Volume Autoencoding for Pretraining Optical Flow Estimation</papertitle>
<br>
<strong>Xiaoyu Shi*</strong>,
Zhaoyang Huang*,
Dasong Li,
Manyuan Zhang, <br>
Kachun Cheung,
Simon See,
Hongwei Qin, <br>
Jifeng Dai,
Hongsheng Li <br>
<em>CVPR</em>, 2023
<br>
<a href="https://arxiv.org/pdf/2303.01237v1.pdf">Paper</a>
/
<a href="https://github.com/XiaoyuShi97/FlowFormerPlusPlus">Code</a>
<p></p>
<p>
Ranks 1st on Sintel Optical Flow benchmark on Mar. 1st, 2023.
</p>
</td>
</tr>
<tr onmouseout="bakedsdf_stop()" onmouseover="bakedsdf_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<img src='images/shiftnet.png' width="320" height="180">
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<papertitle>A Simple Baseline for Video Restoration with Spatial-temporal Shift</papertitle>
<br>
Dasong Li,
<strong>Xiaoyu Shi</strong>,
Yi Zhang,
Kachun Cheung,
Simon See,
Xiaogang Wang,
Hongwei Qin,
Hongsheng Li <br>
<em>CVPR</em>, 2023
<br>
<a href="https://dasongli1.github.io/publication/grouped-shift-net">Project Page</a>
/
<a href="https://arxiv.org/abs/2206.10810">Paper</a>
/
<a href="https://github.com/dasongli1/Shift-Net">Code</a>
<p></p>
<p>
Our approach is based on grouped spatial-temporal shift, which is a lightweight technique that can implicitly capture inter-frame correspondences for multi-frame aggregation.
</p>
</td>
</tr>
<tr onmouseout="bakedsdf_stop()" onmouseover="bakedsdf_start()" bgcolor="#ffffd0">
<td style="padding:20px;width:25%;vertical-align:middle">
<video width=320 height=180 muted autoplay loop>
<source src="images/flowformer-demo.mp4" type="video/mp4" >
Your browser does not support the video tag.
</video>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<papertitle>FlowFormer: A Transformer Architecture for Optical Flow</papertitle>
<br>
Zhaoyang Huang*,
<strong>Xiaoyu Shi*</strong>,
Chao Zhang,
Qiang Wang, <br>
Kachun Cheung,
Hongwei Qin,
Jifeng Dai,
Hongsheng Li <br>
<em>ECCV</em>, 2022
<br>
<a href="https://drinkingcoder.github.io/publication/flowformer">Project Page</a>
/
<a href="https://arxiv.org/abs/2203.16194">Paper</a>
/
<a href="https://github.com/drinkingcoder/FlowFormer-Official">Code</a>
<p></p>
<p>
Ranks 1st on Sintel Optical Flow benchmark on Mar. 17th, 2022.
</p>
</td>
</tr>
<tr onmouseout="bakedsdf_stop()" onmouseover="bakedsdf_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<img src='images/decoupled.png' width="320" height="180">
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<papertitle>Decoupled spatial-temporal transformer for video inpainting</papertitle>
<br>
Rui Liu,
Hanming Deng,
Yangyi Huang,
<strong>Xiaoyu Shi</strong>,
Lewei Lu,
Wenxiu Sun,
Xiaogang Wang,
Jifeng Dai,
Hongsheng Li
<br>
<em>Arxiv</em>, 2021
<br>
<a href="https://arxiv.org/abs/2104.06637">Paper</a>
/
<a href="https://github.com/ruiliu-ai/DSTT">Code</a>
<p></p>
<p>
We propose a novel decoupled spatial-temporal Transformer (DSTT) framework for video inpainting to improve video inpainting quality with higher running efficiency.
</p>
</td>
</tr>
<tr onmouseout="bakedsdf_stop()" onmouseover="bakedsdf_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<img src='images/fuseformer.png' width="320" height="180">
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<papertitle>FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting</papertitle>
<br>
Rui Liu,
Hanming Deng,
Yangyi Huang,
<strong>Xiaoyu Shi</strong>,
Lewei Lu,
Wenxiu Sun,
Xiaogang Wang,
Jifeng Dai,
Hongsheng Li
<br>
<em>ICCV</em>, 2021
<br>
<a href="https://arxiv.org/abs/2109.02974">Paper</a>
/
<a href="https://github.com/ruiliu-ai/FuseFormer">Code</a>
<p></p>
<p>
A Transformer model designed for video inpainting via fine-grained feature fusion based on novel Soft Split and Soft Composition operations.
</p>
</td>
</tr>
<!-- <tr onmouseout="bakedsdf_stop()" onmouseover="bakedsdf_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<img src='images/unified_condition.png' width="160" height="100">
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<papertitle>Context-TAP: Tracking Any Point Demands Spatial Context Features</papertitle>
<br>
Weikang Bian*,
Zhaoyang Huang*,
<strong>Xiaoyu Shi</strong>,
Yitong Dong, <br>
Yijin Li,
HongSheng Li <br>
<em>Arxiv</em>, 2023
<br>
<a href="https://wkbian.github.io/Projects/Context-TAP/">project page</a>
/
<a href="https://arxiv.org/abs/2306.02000">Paper</a>
<p></p>
<p>
We set new SOTA on the task of Tracking Any Point (TAP) by introducing rich spatial context features.
</p>
</td>
</tr> -->
<!-- <tr onmouseout="merf_stop()" onmouseover="merf_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='merf_image'><video width=100% height=100% muted autoplay loop>
<source src="images/merf_after.mp4" type="video/mp4">
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<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://merf42.github.io/">
<papertitle>MERF: Memory-Efficient Radiance Fields for Real-time View Synthesis in Unbounded Scenes</papertitle>
</a>
<br>
<a href="https://creiser.github.io/">Christian Reiser</a>,
<a href="https://szeliski.org/RichardSzeliski.htm">Richard Szeliski</a>,
<a href="https://dorverbin.github.io/">Dor Verbin</a>,
<a href="https://pratulsrinivasan.github.io/">Pratul Srinivasan</a>, <br>
<a href="https://bmild.github.io/">Ben Mildenhall</a>,
<a href="https://www.cvlibs.net/">Andreas Geiger</a>,
<strong>Jonathan T. Barron</strong>,
<a href="https://phogzone.com/">Peter Hedman</a>
<br>
<em>SIGGRAPH</em>, 2023
<br>
<a href="https://merf42.github.io/">project page</a>
/
<a href="https://www.youtube.com/watch?v=3EACM2JAcxc">video</a>
/
<a href="https://arxiv.org/abs/2302.12249">arXiv</a>
<p></p>
<p>
We use volumetric rendering with a sparse 3D feature grid and 2D feature planes to do real-time view synthesis.
</p>
</td>
</tr>
<tr onmouseout="eclipse_stop()" onmouseover="eclipse_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='eclipse_image'><video width=100% height=100% muted autoplay loop>
<source src="images/eclipse_after.mp4" type="video/mp4">
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<img src='images/eclipse_before.jpg' width="160">
</div>
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eclipse_stop()
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</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://dorverbin.github.io/eclipse">
<papertitle>Eclipse: Disambiguating Illumination and Materials using Unintended Shadows</papertitle>
</a>
<br>
<a href="https://dorverbin.github.io/">Dor Verbin</a>,
<a href="https://bmild.github.io/">Ben Mildenhall</a>,
<a href="https://phogzone.com/">Peter Hedman</a>, <br>
<strong>Jonathan T. Barron</strong>,
<a href="Todd Zickler">Todd Zickler</a>,
<a href="https://pratulsrinivasan.github.io/">Pratul Srinivasan</a>
<br>
<em>arXiv</em>, 2023
<br>
<a href="https://dorverbin.github.io/eclipse">project page</a>
/
<a href="https://www.youtube.com/watch?v=amQLGyza3EU">video</a>
/
<a href="https://arxiv.org/abs/2305.16321">arXiv</a>
<p></p>
<p>
Shadows cast by unobserved occluders provide a high-frequency cue for recovering illumination and materials.
</p>
</td>
</tr>
<tr onmouseout="zipnerf_stop()" onmouseover="zipnerf_start()" bgcolor="#ffffd0">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='zipnerf_image'><video width=100% height=100% muted autoplay loop>
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<img src='images/zipnerf.jpg' width="160">
</div>
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</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="http://jonbarron.info/zipnerf">
<papertitle>Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fields</papertitle>
</a>
<br>
<strong>Jonathan T. Barron</strong>,
<a href="https://bmild.github.io/">Ben Mildenhall</a>,
<a href="https://scholar.harvard.edu/dorverbin/home">Dor Verbin</a>,
<a href="https://pratulsrinivasan.github.io/">Pratul Srinivasan</a>,
<a href="https://phogzone.com/">Peter Hedman</a>
<br>
<em>arXiv</em>, 2023
<br>
<a href="http://jonbarron.info/zipnerf">project page</a>
/
<a href="https://www.youtube.com/watch?v=xrrhynRzC8k">video</a>
/
<a href="https://arxiv.org/abs/2304.06706">arXiv</a>
<p></p>
<p>
Combining mip-NeRF 360 and grid-based models like Instant NGP lets us reduce error rates by 8%–77% and accelerate training by 24x.
</p>
</td>
</tr>
<tr onmouseout="db3d_stop()" onmouseover="db3d_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='db3d_image'><video width=100% height=100% muted autoplay loop>
<source src="images/owl.mp4" type="video/mp4">
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<img src='images/owl.png' width="160">
</div>
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</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://dreambooth3d.github.io/">
<papertitle>DreamBooth3D: Subject-Driven Text-to-3D Generation</papertitle>
</a>
<br>
<a href="https://amitraj93.github.io/">Amit Raj</a>, <a href="https://www.linkedin.com/in/srinivas-kaza-64223b74">Srinivas Kaza</a>, <a href="https://poolio.github.io/">Ben Poole</a>, <a href="https://m-niemeyer.github.io/">Michael Niemeyer</a>, <a href="https://natanielruiz.github.io/">Nataniel Ruiz</a>,
<a href="https://bmild.github.io/">Ben Mildenhall</a>, <a href="https://scholar.google.com/citations?user=I2qheksAAAAJ">Shiran Zada</a>, <a href="https://kfiraberman.github.io/">Kfir Aberman</a>, <a href="http://people.csail.mit.edu/mrub/">Michael Rubinstein</a>,
<strong>Jonathan T. Barron</strong>, <a href="http://people.csail.mit.edu/yzli/">Yuanzhen Li</a>, <a href="https://varunjampani.github.io/">Varun Jampani</a>
<br>
<em>arXiv</em>, 2023
<br>
<a href="https://dreambooth3d.github.io/">project page</a> /
<a href="https://arxiv.org/abs/2303.13508">arXiv</a>
<p></p>
<p>Combining DreamBooth (personalized text-to-image) and DreamFusion (text-to-3D) yields high-quality, subject-specific 3D assets with text-driven modifications</p>
</td>
</tr>
<tr onmouseout="alignerf_stop()" onmouseover="alignerf_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='alignerf_image'>
<img src='images/alignerf_after.jpg' width="160"></div>
<img src='images/alignerf_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://yifanjiang19.github.io/alignerf">
<papertitle>AligNeRF: High-Fidelity Neural Radiance Fields via Alignment-Aware Training
</papertitle>
</a>
<br>
<a href="https://yifanjiang.net/">Yifan Jiang</a>,
<a href="https://phogzone.com/">Peter Hedman</a>,
<a href="https://bmild.github.io/">Ben Mildenhall</a>,
<a href="https://ir1d.github.io/">Dejia Xu</a>, <br>
<strong>Jonathan T. Barron</strong>,
<a href="https://spark.adobe.com/page/CAdrFMJ9QeI2y/">Zhangyang Wang</a>,
<a href="https://tianfan.info/">Tianfan Xue</a>
<br>
<em>CVPR</em>, 2023
<br>
<a href="https://yifanjiang19.github.io/alignerf">project page</a>
/
<a href="https://arxiv.org/abs/2211.09682">arXiv</a>
<p></p>
<p>
Accounting for misalignment due to scene motion or calibration errors improves NeRF reconstruction quality.
</p>
</td>
</tr>
<tr onmouseout="dreamfusion_stop()" onmouseover="dreamfusion_start()" bgcolor="#ffffd0">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='dreamfusion_image'><video width=100% height=100% muted autoplay loop>
<source src="images/dreamfusion.mp4" type="video/mp4">
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<img src='images/dreamfusion.jpg' width="160">
</div>
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</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://dreamfusion3d.github.io/">
<papertitle>DreamFusion: Text-to-3D using 2D Diffusion</papertitle>
</a>
<br>
<a href="https://cs.stanford.edu/~poole/">Ben Poole</a>,
<a href="https://www.ajayj.com/">Ajay Jain</a>,
<strong>Jonathan T. Barron</strong>,
<a href="https://bmild.github.io/">Ben Mildenhall</a>
<br>
<em>ICLR</em>, 2023   <font color="red"><strong>(Oral Presentation, Outstanding Paper Award)</strong></font>
<br>
<a href="https://dreamfusion3d.github.io/">project page</a>
/
<a href="https://arxiv.org/abs/2209.14988">arXiv</a>
/
<a href="https://dreamfusion3d.github.io/gallery.html">gallery</a>
<p></p>
<p>
We optimize a NeRF from scratch using a pretrained text-to-image diffusion model to do text-to-3D generative modeling.
</p>
</td>
</tr>
<tr onmouseout="mira_stop()" onmouseover="mira_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='mira_image'>
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</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://openreview.net/forum?id=AmPeAFzU3a4">
<papertitle>MIRA: Mental Imagery for Robotic Affordances</papertitle>
</a>
<br>
<a href="https://yenchenlin.me/">Lin Yen-Chen</a>,
<a href="http://www.peteflorence.com/">Pete Florence</a>,
<a href="https://andyzeng.github.io/">Andy Zeng</a>, <strong>Jonathan T. Barron</strong>,
<a href="https://yilundu.github.io/">Yilun Du</a>,
<a href="https://people.csail.mit.edu/weichium/">Wei-Chiu Ma</a>,
<a href="https://anthonysimeonov.github.io/">Anthony Simeonov</a>,
<a href="https://meche.mit.edu/people/faculty/[email protected]">Alberto Rodriguez</a>,
<a href="http://web.mit.edu/phillipi/">Phillip Isola</a>
<br>
<em>CoRL</em>, 2022
<p></p>
<p>
NeRF lets us synthesize novel orthographic views that work well with pixel-wise algorithms for robotic manipulation.
</p>
</td>
</tr>
<tr onmouseout="samurai_stop()" onmouseover="samurai_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='samurai_image'>
<img src='images/samurai_after.jpg' width="160"></div>
<img src='images/samurai_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://markboss.me/publication/2022-samurai/">
<papertitle>SAMURAI: Shape And Material from Unconstrained Real-world Arbitrary Image Collections</papertitle>
</a>
<br>
<a href="https://markboss.me">Mark Boss</a>,
<a href="">Andreas Engelhardt</a>,
<a href="https://abhishekkar.info/">Abhishek Kar</a>,
<a href="http://people.csail.mit.edu/yzli/">Yuanzhen Li</a>,
<a href="https://deqings.github.io/">Deqing Sun</a>,
<strong>Jonathan T. Barron</strong>,
<a href="https://uni-tuebingen.de/en/faculties/faculty-of-science/departments/computer-science/lehrstuehle/computergrafik/computer-graphics/staff/prof-dr-ing-hendrik-lensch/">Hendrik P. A. Lensch</a>,
<a href="https://varunjampani.github.io">Varun Jampani</a>
<br>
<em>NeurIPS</em>, 2022
<br>
<a href="https://markboss.me/publication/2022-samurai/">project page</a> /
<a href="https://www.youtube.com/watch?v=LlYuGDjXp-8">video</a> /
<a href="https://arxiv.org/abs/2205.15768">arXiv</a>
<p></p>
<p>
A joint optimization framework for estimating shape, BRDF, camera pose, and illumination from in-the-wild image collections.
</p>
</td>
</tr>
<tr onmouseout="pnf_stop()" onmouseover="pnf_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='pnf_image'>
<img src='images/pnf_before.jpg' width="160"></div>
<img src='images/pnf_after.jpg' width="160">
</div>
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</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="TODO">
<papertitle>Polynomial Neural Fields for Subband Decomposition</papertitle>
</a> <br>
<a href="https://www.guandaoyang.com/">Guandao Yang*</a>,
<a href="https://sagiebenaim.github.io/">Sagie Benaim*</a>,
<a href="https://varunjampani.github.io/">Varun Jampani</a>,
<a href="https://www.kylegenova.com/">Kyle Genova</a>,
<strong>Jonathan T. Barron</strong>,
<a href="https://www.cs.princeton.edu/~funk/">Thomas Funkhouser</a>,
<a href="http://home.bharathh.info/">Bharath Hariharan</a>,
<a href="https://sergebelongie.github.io/">Serge Belongie</a>
<br>
<em>NeurIPS</em>, 2022
<p>
Representing neural fields as a composition of manipulable and interpretable components lets you do things like reason about frequencies and scale.
</p>
</td>
</tr>
<tr onmouseout="malle_stop()" onmouseover="malle_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='malle_image'>
<img src='images/MalleConv_after.jpg' width="160"></div>
<img src='images/MalleConv_before.jpg' width="160">
</div>
<script type="text/javascript">
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</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://yifanjiang.net/MalleConv.html">
<papertitle>Fast and High-Quality Image Denoising via Malleable Convolutions</papertitle>
</a>
<br>
<a href="https://yifanjiang.net/">Yifan Jiang</a>,
<a href="https://bartwronski.com/">Bartlomiej Wronski</a>,
<a href="https://bmild.github.io/">Ben Mildenhall</a>, <br>
<strong>Jonathan T. Barron</strong>,
<a href="https://spark.adobe.com/page/CAdrFMJ9QeI2y/">Zhangyang Wang</a>,
<a href="https://tianfan.info/">Tianfan Xue</a>
<br>
<em>ECCV</em>, 2022
<br>
<a href="https://yifanjiang.net/MalleConv.html">project page</a>
/
<a href="https://arxiv.org/abs/2201.00392">arXiv</a>
<p></p>
<p>
We denoise images efficiently by predicting spatially-varying kernels at low resolution and using a fast fused op to jointly upsample and apply these kernels at full resolution.
</p>
</td>
</tr>
<tr onmouseout="nerfsuper_stop()" onmouseover="nerfsuper_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='nerfsuper_image'><video width=100% height=100% muted autoplay loop>
<source src="images/nerf_supervision.mp4" type="video/mp4">
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<img src='images/nerf_supervision.jpg' width="160">
</div>
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</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="http://yenchenlin.me/nerf-supervision/">
<papertitle>NeRF-Supervision: Learning Dense Object Descriptors from Neural Radiance Fields</papertitle>
</a>
<br>
<a href="https://yenchenlin.me/">Lin Yen-Chen</a>,
<a href="http://www.peteflorence.com/">Pete Florence</a>,
<strong>Jonathan T. Barron</strong>, <br>
<a href="https://scholar.google.com/citations?user=_BPdgV0AAAAJ&hl=en">Tsung-Yi Lin</a>,
<a href="https://meche.mit.edu/people/faculty/[email protected]">Alberto Rodriguez</a>,
<a href="http://web.mit.edu/phillipi/">Phillip Isola</a>
<br>
<em>ICRA</em>, 2022
<br>
<a href="http://yenchenlin.me/nerf-supervision/">project page</a> /
<a href="https://arxiv.org/abs/2203.01913">arXiv</a> /
<a href="https://www.youtube.com/watch?v=_zN-wVwPH1s">video</a> /
<a href="https://github.com/yenchenlin/nerf-supervision-public">code</a> /
<a href="https://colab.research.google.com/drive/13ISri5KD2XeEtsFs25hmZtKhxoDywB5y?usp=sharing">colab</a>
<p></p>
<p>NeRF works better than RGB-D cameras or multi-view stereo when learning object descriptors.</p>
</td>
</tr>
<tr onmouseout="refnerf_stop()" onmouseover="refnerf_start()" bgcolor="#ffffd0">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='refnerf_image'><video width=100% height=100% muted autoplay loop>
<source src="images/refnerf.mp4" type="video/mp4">
Your browser does not support the video tag.
</video></div>
<img src='images/refnerf.jpg' width="160">
</div>
<script type="text/javascript">
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</script>
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://dorverbin.github.io/refnerf/index.html">
<papertitle>Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fields</papertitle>
</a>
<br>
<a href="https://scholar.harvard.edu/dorverbin/home">Dor Verbin</a>,
<a href="https://phogzone.com/">Peter Hedman</a>,
<a href="https://bmild.github.io/">Ben Mildenhall</a>, <br>
<a href="Todd Zickler">Todd Zickler</a>,
<strong>Jonathan T. Barron</strong>,
<a href="https://pratulsrinivasan.github.io/">Pratul Srinivasan</a>
<br>
<em>CVPR</em>, 2022   <font color="red"><strong>(Oral Presentation, Best Student Paper Honorable Mention)</strong></font>
<br>
<a href="https://dorverbin.github.io/refnerf/index.html">project page</a>
/
<a href="https://arxiv.org/abs/2112.03907">arXiv</a>
/
<a href="https://youtu.be/qrdRH9irAlk">video</a>
<p></p>
<p>Explicitly modeling reflections in NeRF produces realistic shiny surfaces and accurate surface normals, and lets you edit materials.</p>
</td>
</tr>
<tr onmouseout="mip360_stop()" onmouseover="mip360_start()" bgcolor="#ffffd0">
<td style="padding:20px;width:25%;vertical-align:middle">
<div class="one">
<div class="two" id='mip360_image'><video width=100% height=100% muted autoplay loop>
<source src="images/mip360_sat.mp4" type="video/mp4">
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<img src='images/mip360_sat.jpg' width="160">
</div>
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</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="http://jonbarron.info/mipnerf360">
<papertitle>Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields</papertitle>
</a>
<br>
<strong>Jonathan T. Barron</strong>,
<a href="https://bmild.github.io/">Ben Mildenhall</a>,
<a href="https://scholar.harvard.edu/dorverbin/home">Dor Verbin</a>,
<a href="https://pratulsrinivasan.github.io/">Pratul Srinivasan</a>,
<a href="https://phogzone.com/">Peter Hedman</a>
<br>
<em>CVPR</em>, 2022   <font color="red"><strong>(Oral Presentation)</strong></font>
<br>
<a href="http://jonbarron.info/mipnerf360">project page</a>
/
<a href="https://arxiv.org/abs/2111.12077">arXiv</a>
/
<a href="https://youtu.be/zBSH-k9GbV4">video</a>
<p></p>
<p>mip-NeRF can be extended to produce realistic results on unbounded scenes.</p>
</td>
</tr>