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<!DOCTYPE html>
<html>
<head lang="en">
<meta charset="UTF-8">
<meta http-equiv="x-ua-compatible" content="ie=edge">
<title>SceneDreamer</title>
<meta name="description" content="Project page for SceneDreamer: Unbounded 3D Scene Generation from 2D Image Collections">
<meta name="viewport" content="width=device-width, initial-scale=1">
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<meta property="og:image" content="https://scene-dreamer.github.io/img/teaser.jpg">
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<meta property="og:type" content="website" />
<meta property="og:url" content="https://scene-dreamer.github.io"/>
<meta property="og:title" content="SceneDreamer" />
<meta property="og:description" content="Project page for SceneDreamer: Unbounded 3D Scene Generation from 2D Image Collections" />
<!--TWITTER-->
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<meta name="twitter:description" content="Project page for SceneDreamer: Unbounded 3D Scene Generation from 2D Image Collections" />
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else
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</script>
</head>
<body>
<div class="container" id="main" style="width: 100%; max-width: 1500px;">
<div class="row">
<h1 class="col-md-12 text-center">
<b>SceneDreamer</b>: Unbounded 3D Scene Generation from 2D Image Collections <br>
<small>
TPAMI 2023
</small>
</h1>
</div>
<div class="row">
<div class="col-md-12 text-center">
<ul class="list-inline">
<li>
<a href="https://frozenburning.github.io">
Zhaoxi Chen
</a>
</li>
<li>
<a href="https://wanggcong.github.io/">
Guangcong Wang
</a>
</li>
<li>
<a href="http://liuziwei7.github.io">
Ziwei Liu
</a>
</li>
</br>Nanyang Technological University
</ul>
</div>
</div>
<div class="row">
<div class="col-md-4 col-md-offset-4 text-center">
<ul class="nav nav-pills nav-justified">
<li>
<a href="https://arxiv.org/abs/2302.01330">
<image src="https://uploads-ssl.webflow.com/51e0d73d83d06baa7a00000f/5cab99df4998decfbf9e218e_paper-01-p-500.png" height="60px">
<h4><strong>Paper</strong></h4>
</a>
</li>
<li>
<a href="https://youtu.be/nEfSKL2_FoA">
<image src="img/youtube_icon.png" height="60px">
<h4><strong>Video</strong></h4>
</a>
</li>
<li>
<a href="https://github.com/FrozenBurning/SceneDreamer">
<image src="img/github.png" height="60px">
<h4><strong>Code</strong></h4>
</a>
</li>
<li>
<a href="https://huggingface.co/spaces/FrozenBurning/SceneDreamer">
<image src="img/hf-icon.png" height="60px">
<h4><strong>Demo</strong></h4>
</a>
</li>
</ul>
</div>
</div>
<div class="row text-center" style="font-size:large;"><strong>TL;DR:</strong> SceneDreamer learns to generate unbounded 3D scenes from in-the-wild 2D image collections. <br> Our method can synthesize diverse landscapes across different styles, with 3D consistency, well-defined depth, and free camera trajectory.
</div>
<div class="row">
<div class="col-md-8 col-md-offset-2">
<video id="v0" width="100%" autoplay loop muted controls>
<source src="img/teaser.mp4" type="video/mp4"/>
</video>
</div>
</div>
<div class="row">
<div class="col-md-8 col-md-offset-2">
<h3>
Abstract
</h3>
<p class="text-justify">
In this work, we present <strong>SceneDreamer</strong>, an unconditional generative model for unbounded 3D scenes, which synthesizes large-scale 3D landscapes from random noises. Our framework is learned from in-the-wild 2D image collections only, without any 3D annotations.
At the core of SceneDreamer is a principled learning paradigm comprising <strong>1)</strong> an efficient yet expressive 3D scene representation, <strong>2)</strong> a generative scene parameterization, and <strong>3)</strong> an effective renderer that can leverage the knowledge from 2D images.
Our framework starts from an efficient bird's-eye-view (BEV) representation generated from simplex noise, which consists of a height field and a semantic field. The height field represents the surface elevation of 3D scenes, while the semantic field provides detailed scene semantics. This BEV scene representation enables 1) representing a 3D scene with quadratic complexity, 2) disentangled geometry and semantics, and 3) efficient training. Furthermore, we propose a novel generative neural hash grid to parameterize the latent space given 3D positions and the scene semantics, which aims to encode generalizable features across scenes and align content. Lastly, a neural volumetric renderer, learned from 2D image collections through adversarial training, is employed to produce photorealistic images. Extensive experiments demonstrate the effectiveness of SceneDreamer and superiority over state-of-the-art methods in generating vivid yet diverse unbounded 3D worlds.
</p>
</div>
</div>
<br>
<div class="row">
<div class="col-md-8 col-md-offset-2">
<h3>
Gallery
</h3>
<p>
<u>
Recommend to enter full screen for better visual quality
</u>
</p>
<table>
<tbody>
<tr style="display:table-row; text-align: center;">
<td>
<video id="v4" width="100%" autoplay loop muted controls>
<source src="img/inf.mp4" type="video/mp4"/>
</video>
</td>
</tr>
<tr style="display:table-row; text-align: center;">
<td>
<video id="v2" width="100%" autoplay loop muted controls>
<source src="img/gen_results.mp4" type="video/mp4"/>
</video>
</td>
</tr>
<tr style="display:table-row; text-align: center;">
<td>
<video id="v3" width="100%" autoplay loop muted controls>
<source src="img/free_cam.mp4" type="video/mp4"/>
</video>
</td>
<br>
</tr>
<tr style="display:table-row; text-align: center;">
<td>
<video id="v4" width="100%" autoplay loop muted controls>
<source src="img/interp.mp4" type="video/mp4"/>
</video>
</td>
</tr>
</tbody></table>
</div>
</div>
<br>
<div class="row">
<div class="col-md-8 col-md-offset-2">
<h3>
Framework
</h3>
<br>
<video id="v1" width="100%" autoplay loop muted controls>
<source src="img/method.mp4" type="video/mp4"/>
</video>
<p class="text-justify">
Given a simplex noise and a style code as input, our model is capable of synthesizing large-scale 3D scenes where the camera can move freely and get realistic renderings. We first derive our BEV scene representation which consists of a height field and a semantic field. Then, we use a generative neural hash grid to parameterize the hyperspace of space-varied and scene-varied latent features given scene semantics and 3D position. Finally, a style-modulated renderer is employed to blend latent features and render 2D images via volume rendering. The entire framework is trained on in-the-wild 2D images end-to-end.
</p>
</div>
</div>
<div class="row">
<div class="col-md-8 col-md-offset-2">
<h3>
Video
</h3>
<div class="text-center">
<div style="position:relative;padding-top:56.25%;">
<iframe width="560" height="315" src="https://www.youtube.com/embed/nEfSKL2_FoA" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen style="position:absolute;top:0;left:0;width:100%;height:100%;"></iframe>
</div>
</div>
</div>
</div>
<div class="row">
<div class="col-md-8 col-md-offset-2">
<h3>
Citation
</h3>
<div class="form-group col-md-10" style="padding: 0%; margin: 0%;">
<textarea id="bibtex" class="form-control" readonly>
@ARTICLE {chen2023sd,
author = {Z. Chen and G. Wang and Z. Liu},
journal = {IEEE Transactions on Pattern Analysis & Machine Intelligence},
title = {SceneDreamer: Unbounded 3D Scene Generation From 2D Image Collections},
year = {2023},
volume = {45},
number = {12},
issn = {1939-3539},
pages = {15562-15576},
doi = {10.1109/TPAMI.2023.3321857},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
month = {dec}
}</textarea>
</div>
</div>
</div>
<div class="row">
<div class="col-md-8 col-md-offset-2">
<h3>
Related Links
</h3>
<p class="text-justify">
<a href="https://frozenburning.github.io/projects/text2light/">Text2Light</a> generates HDR panorama from texts with a resolution up to 4K. <br>
<a href="https://style-light.github.io/">StyleLight</a> generates HDR indoor panorama from a limited FOV image. <br>
<a href="https://hongfz16.github.io/projects/EVA3D.html">EVA3D</a> is a 3D human generative model that only requires 2D image collections for training. <br>
</p>
</div>
<div class="row">
<div class="col-md-8 col-md-offset-2">
<h3>
Acknowledgements
</h3>
<p class="text-justify">
This work is supported by the National Research Foundation, Singapore under its AI Singapore Programme, NTU NAP, MOE AcRF Tier 2 (T2EP20221-0033), and under the RIE2020 Industry Alignment Fund - Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from the industry partner(s).
<br>
The website template is borrowed from <a href="https://jonbarron.info/mipnerf/">Mip-NeRF</a>.
</p>
<br>
</div>
</div>
</div>
</body>
</html>