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<h1 class="post-title">
Yiyi Liao
</h1>
<p class="desc">Zhejiang University</p>
</header>
<article>
<div class="profile float-right">
<img class="img-fluid z-depth-1 rounded" src="" srcset="/assets/img/prof_pic.jpg 384w">
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<div class="clearfix">
<p>I am an assistant professor in <a href="https://en.wikipedia.org/wiki/Zhejiang_University" target="_blank" rel="noopener noreferrer">Zhejiang University</a>, leading the <a href="https://yiyiliao.github.io/xdlab">X-Dimensional Representations Lab (X-D Lab)</a>. Before that, I was a Postdoc in <a href="http://cvlibs.net/" target="_blank" rel="noopener noreferrer">Autonomous Vision Group</a> at the University of Tübingen and the MPI for Intelligent Systems, working with <a href="https://avg.is.tuebingen.mpg.de/person/ageiger" target="_blank" rel="noopener noreferrer">Prof. Andreas Geiger</a>. I received my Ph.D. in Control Science and Engineering from <a href="https://en.wikipedia.org/wiki/Zhejiang_University" target="_blank" rel="noopener noreferrer">Zhejiang University</a> in June 2018 and the B.S. degree from <a href="https://en.wikipedia.org/wiki/Xi%27an_Jiaotong_University" target="_blank" rel="noopener noreferrer">Xi’an Jiaotong University</a> in 2013.</p>
<p>My research interest lies in 3D computer vision, including scene understanding, 3D reconstruction and 3D generative models.</p>
<p>For prospective students interested in computer vision, feel free to contact me via <a href="mailto:[email protected]">email</a>!</p>
</div>
<div class="news">
<h2>news</h2>
<div class="table-responsive" style="height: 250px; overflow-y:scroll">
<table class="table table-sm table-borderless" style="width: 100%">
<colgroup>
<col span="1" style="width: 15%;">
<col span="1" style="width: 85%;">
</colgroup>
<!-- Put <thead>, <tbody>, and <tr>'s here! -->
<tbody>
<tr>
<th scope="row">Jul 1, 2024</th>
<td>
<a href="https://xdimlab.github.io/TeFF/" target="_blank" rel="noopener noreferrer">TEFF</a>, <a href="https://xdimlab.github.io/EDUS/" target="_blank" rel="noopener noreferrer">EDUS</a>, and <a href="https://xdimlab.github.io/REFRAME/" target="_blank" rel="noopener noreferrer">REFRAME</a> are accepted to ECCV 2024.
</td>
</tr>
<tr>
<th scope="row">May 16, 2024</th>
<td>
<a href="https://arxiv.org/pdf/2311.09525.pdf" target="_blank" rel="noopener noreferrer">NGEL-SLAM</a> won the ICRA Best Paper Award in Robot Vision!
</td>
</tr>
<tr>
<th scope="row">Mar 21, 2024</th>
<td>
I will serve as a Program Chair for 3DV 2025.
</td>
</tr>
<tr>
<th scope="row">Feb 27, 2024</th>
<td>
Two papers are accepted to CVPR 2024.
</td>
</tr>
<tr>
<th scope="row">Jul 15, 2023</th>
<td>
Three papers are accepted to ICCV 2023.
</td>
</tr>
<tr>
<th scope="row">Mar 17, 2023</th>
<td>
I will serve as an Area Chair for NeurIPS 2023.
</td>
</tr>
<tr>
<th scope="row">Feb 28, 2023</th>
<td>
Three papers are accepted to CVPR 2023.
</td>
</tr>
<tr>
<th scope="row">Sep 16, 2022</th>
<td>
Our <a href="https://katjaschwarz.github.io/voxgraf/" target="_blank" rel="noopener noreferrer">VoxGRAF</a> is accepted to NeurIPS 2022.
</td>
</tr>
<tr>
<th scope="row">Sep 14, 2022</th>
<td>
I will serve as an Area Chair for CVPR 2023.
</td>
</tr>
<tr>
<th scope="row">Sep 11, 2022</th>
<td>
One paper is accepted to CoRL 2022.
</td>
</tr>
<tr>
<th scope="row">Aug 5, 2022</th>
<td>
Our <a href="https://fuxiao0719.github.io/projects/panopticnerf/" target="_blank" rel="noopener noreferrer">Panoptic NeRF</a> is accepted to 3DV 2022.
</td>
</tr>
<tr>
<th scope="row">Jul 5, 2022</th>
<td>
Our work on <a href="https://arxiv.org/abs/2203.13572" target="_blank" rel="noopener noreferrer">category-level object pose estimation</a> leveraging 3D-aware generative models is accepted to ECCV 2022.
</td>
</tr>
<tr>
<th scope="row">Jun 6, 2022</th>
<td>
I was invited to give a talk at the <a href="https://sites.google.com/view/3d-dlad-v4-iv2022/" target="_blank" rel="noopener noreferrer">3D-DLAD workshop</a> at IV 2022.
</td>
</tr>
<tr>
<th scope="row">Jun 4, 2022</th>
<td>
Our KITTI-360 is accepted to TPAMI and we released all benchmarks! Check out our <a href="https://autonomousvision.github.io/kitti-360/" target="_blank" rel="noopener noreferrer">blog</a> for more information of the benchmarks.
</td>
</tr>
<tr>
<th scope="row">Apr 15, 2022</th>
<td>
I will serve as an Area Chair for 3DV 2022.
</td>
</tr>
<tr>
<th scope="row">Sep 29, 2021</th>
<td>
Two papers (1 <a href="http://www.cvlibs.net/publications/Peng2021NEURIPS.pdf" target="_blank" rel="noopener noreferrer">oral</a>, 1 <a href="http://www.cvlibs.net/publications/Schwarz2021NEURIPS.pdf" target="_blank" rel="noopener noreferrer">poster</a>) are accepted to NeurIPS 2021.
</td>
</tr>
<tr>
<th scope="row">Jun 24, 2021</th>
<td>
Our work <a href="https://github.com/fabiotosi92/SMD-Nets" target="_blank" rel="noopener noreferrer">SMD-Nets</a> was featured on the <a href="https://www.rsipvision.com/CVPR2021-Wednesday/6/" target="_blank" rel="noopener noreferrer">CVPR Daily</a> and the <a href="https://www.rsipvision.com/ComputerVisionNews-2021July/30/" target="_blank" rel="noopener noreferrer">BEST OF CVPR of Computer Vision News</a>.
</td>
</tr>
<tr>
<th scope="row">Jun 2, 2021</th>
<td>
I will be joining <a href="https://en.wikipedia.org/wiki/Zhejiang_University" target="_blank" rel="noopener noreferrer">Zhejiang University</a> as a tenure-track assistant professor this September!
</td>
</tr>
<tr>
<th scope="row">May 31, 2021</th>
<td>
I will serve as an Area Chair for BMVC 2021.
</td>
</tr>
<tr>
<th scope="row">May 20, 2021</th>
<td>
I was acknowledged as <a href="http://cvpr2021.thecvf.com/node/184" target="_blank" rel="noopener noreferrer">Outstanding Reviewer</a> at CVPR 2021.
</td>
</tr>
<tr>
<th scope="row">Dec 25, 2020</th>
<td>
I was invited to give a talk at Graphics And Mixed Environment Seminar (GAMES). Check the <a href="https://yiyiliao.github.io/20201224_GAMES/">interactive slides</a> on 3D controllable image synthesis.
</td>
</tr>
</tbody>
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<div class="publications">
<h2>selected publications</h2>
<p> Full publication list can be found on <a href="https://scholar.google.com/citations?user=lTBMax0AAAAJ&hl" target="_blank" rel="noopener noreferrer">Google Scholar</a>. <br>
<sup>*</sup>equal contribution; <sup>♯</sup>corresponding author. </p>
<h2 class="year">2024</h2>
<ol class="bibliography">
<li>
<div class="row">
<div class="col-md-3">
<div class="img-fluid rounded">
<img src="/assets/teaser/Wu2024RAL.jpg" alt="DORec: Decomposed Object Reconstruction and Segmentation Utilizing 2D Self-Supervised Features" style="width: 100%;">
</div>
</div>
<div id="Wu2024RAL" class="col-md-9">
<div class="title">DORec: Decomposed Object Reconstruction and Segmentation Utilizing 2D Self-Supervised Features</div>
<div class="author">
Wu, Jun,
Li, Sicheng,
Ji, Sihui,
Yang, Yifei,
<a href="https://ywang-zju.github.io/" target="_blank" rel="noopener noreferrer">Wang, Yue</a>,
Xiong, Rong<sup>♯</sup>,
and <em>Liao, Yiyi<sup>♯</sup></em>
</div>
<div class="periodical">
<em>In IEEE Robotics and Automation Letters (RA-L)</em>
2024
</div>
<div class="links">
<a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
<a href="https://arxiv.org/pdf/2310.11092.pdf" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">PDF</a>
</div>
<!-- Hidden abstract block -->
<div class="abstract hidden">
<p>Recovering 3D geometry and textures of individual objects is crucial for many robotics applications, such as manipulation, pose estimation, and autonomous driving. However, decomposing a target object from a complex background is challenging. Most existing approaches rely on costly manual labels to acquire object instance perception. Recent advancements in 2D self-supervised learning offer new prospects for identifying objects of interest, yet leveraging such noisy 2D features for clean decomposition remains difficult. In this paper, we propose a Decomposed Object Reconstruction (DORec) network based on neural implicit representations. Our key idea is to use 2D self-supervised features to create two levels of masks for supervision: a binary mask for foreground regions and a K-cluster mask for semantically similar regions. These complementary masks result in robust decomposition. Experimental results on different datasets show DORec’s superiority in segmenting and reconstructing diverse foreground objects from varied backgrounds enabling downstream tasks such as pose estimation.</p>
</div>
<!-- Hidden bibtex block -->
</div>
</div>
</li>
<li>
<div class="row">
<div class="col-md-3">
<div class="img-fluid rounded">
<img src="/assets/teaser/Chen2024ECCV.gif" alt="TeFF: Learning 3D-Aware GANs from Unposed Images with Template Feature Field" style="width: 100%;">
</div>
</div>
<div id="Chen2024ECCV" class="col-md-9">
<div class="title">
<span>TeFF: Learning 3D-Aware GANs from Unposed Images with Template Feature Field</span>
<span style="color:red;">(Oral)</span>
</div>
<div class="author">
<a href="https://xinyachen21.github.io/" target="_blank" rel="noopener noreferrer">Chen, Xinya</a>,
Guo, Hanlei,
Bin, Yanrui,
<a href="https://zhanghe3z.github.io/" target="_blank" rel="noopener noreferrer">Zhang, Shangzhan</a>,
Yang, Yuanbo,
<a href="https://ywang-zju.github.io/" target="_blank" rel="noopener noreferrer">Wang, Yue</a>,
<a href="https://shenyujun.github.io/" target="_blank" rel="noopener noreferrer">Shen, Yujun</a>,
and <em>Liao, Yiyi<sup>♯</sup></em>
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<div class="periodical">
<em>In Proc. of the European Conf. on Computer Vision (ECCV)</em>
2024
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<div class="links">
<a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
<a href="https://arxiv.org/pdf/2404.05705.pdf" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">PDF</a>
<a href="https://github.com/XinyaChen21/TeFF/" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">Code</a>
<a href="https://xdimlab.github.io/TeFF/" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">Website</a>
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<p>Collecting accurate camera poses of training images has been shown to well serve the learning of 3D-aware generative adversarial networks (GANs) yet can be quite expensive in practice. This work targets learning 3D-aware GANs from unposed images, for which we propose to perform on-the-fly pose estimation of training images with a learned template feature field (TeFF). Concretely, in addition to a generative radiance field as in previous approaches, we ask the generator to also learn a field from 2D semantic features while sharing the density from the radiance field. Such a framework allows us to acquire a canonical 3D feature template leveraging the dataset mean discovered by the generative model, and further efficiently estimate the pose parameters on real data. Experimental results on various challenging datasets demonstrate the superiority of our approach over state-of-the-art alternatives from both the qualitative and the quantitative perspectives.</p>
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<img src="/assets/teaser/Ji2024ECCV.jpg" alt="REFRAME: REFlective Surface ReAl-Time Rendering for MobilE Devices" style="width: 100%;">
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<div class="title">REFRAME: REFlective Surface ReAl-Time Rendering for MobilE Devices</div>
<div class="author">
Ji, Chaojie,
Li, Yufeng,
and <em>Liao, Yiyi<sup>♯</sup></em>
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<div class="periodical">
<em>In Proc. of the European Conf. on Computer Vision (ECCV)</em>
2024
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<div class="links">
<a class="abstract btn btn-sm z-depth-0" role="button">Abs</a>
<a href="https://arxiv.org/pdf/2403.16481.pdf" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">PDF</a>
<a href="https://github.com/MARVELOUSJI/REFRAME" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">Code</a>
<a href="https://xdimlab.github.io/REFRAME/" class="btn btn-sm z-depth-0" role="button" target="_blank" rel="noopener noreferrer">Website</a>
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<p>This work tackles the challenging task of achieving real-time novel view synthesis for reflective surfaces across various scenes. Existing real-time rendering methods, especially those based on meshes, often have subpar performance in modeling surfaces with rich view-dependent appearances. Our key idea lies in leveraging meshes for rendering acceleration while incorporating a novel approach to parameterize view-dependent information. We decompose the color into diffuse and specular, and model the specular color in the reflected direction based on a neural environment map. Our experiments demonstrate that our method achieves comparable reconstruction quality for highly reflective surfaces compared to state-of-the-art offline methods, while also efficiently enabling real-time rendering on edge devices such as smartphones.</p>
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<img src="/assets/teaser/Miao2024ECCV.gif" alt="EDUS: Efficient Depth-Guided Urban View Synthesis" style="width: 100%;">
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<div class="title">EDUS: Efficient Depth-Guided Urban View Synthesis</div>
<div class="author">
Miao, Sheng<sup>*</sup>,
<a href="https://jaceyhuang.github.io/" target="_blank" rel="noopener noreferrer">Huang, Jiaxin<sup>*</sup></a>,