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<!DOCTYPE html>
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<head>
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<meta name="description"
content="One-2-3-45++: Fast Single Image to 3D Objects with Consistent Multi-View Generation and 3D Diffusion.">
<meta name="keywords" content="3D AIGC, single image to 3D, single image reconstruction, text to 3D">
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<title>One-2-3-45++</title>
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Related Research
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<a class="navbar-item" href="https://one-2-3-45.github.io">
One-2-3-45
</a>
<a class="navbar-item" href="https://github.com/cvlab-columbia/zero123">
Zero123
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<a class="navbar-item" href="https://github.com/SUDO-AI-3D/zero123plus">
Zero123++
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<section class="hero">
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<h2 class="title is-1 publication-title">One-2-3-45++: Fast Single Image to 3D Objects with Consistent Multi-View Generation and 3D Diffusion</h2>
<h4 class="title is-4">CVPR 2024</h4>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://cseweb.ucsd.edu/~mil070/">Minghua Liu</a><sup>1*</sup>,</span>
<span class="author-block">
<a href="https://rshi.top">Ruoxi Shi</a><sup>1*</sup>,</span>
<span class="author-block">
<a href="https://ootts.github.io">Linghao Chen</a><sup>1,2*^</sup>,
</span>
<span class="author-block">
<a href="https://github.com/zhuoyang20">Zhuoyang Zhang</a><sup>3*^</sup>,
</span>
<span class="author-block">
<a href="https://chaoxu.xyz">Chao Xu</a><sup>4*</sup>,
</span>
<span class="author-block">
<a href="https://sarahweiii.github.io">Xinyue Wei</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://lakonik.github.io">Hansheng Chen</a><sup>5^</sup>,
</span>
<span class="author-block">
<a href="https://www.ncj.wiki">Chong Zeng</a><sup>2^</sup>,
</span>
<span class="author-block">
<a href="https://cseweb.ucsd.edu/~jigu/">Jiayuan Gu</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://cseweb.ucsd.edu//~haosu/">Hao Su</a><sup>1</sup>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>UC San Diego,</span>
<span class="author-block"><sup>2</sup>Zhejiang University,</span>
<span class="author-block"><sup>3</sup>Tsinghua University,</span>
<span class="author-block"><sup>4</sup>UCLA,</span>
<span class="author-block"><sup>5</sup>Stanford University</span>
</div>
<div class="is-size-6 publication-authors">
<span class="author-block">* Equal contribution.</span>
</div>
<div class="is-size-6 publication-authors">
<span class="author-block">^ Work done during internship at UC San Diego.</span>
</div>
<div class="column has-text-centered">
<div class="publication-links">
<!-- PDF Link. -->
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<a href="https://arxiv.org/pdf/2311.07885.pdf"
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>
<!-- Code Link. -->
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<a href="https://github.com/SUDO-AI-3D/One2345plus"
class="external-link button is-normal is-rounded is-dark">
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<span>Code</span>
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class="external-link button is-normal is-rounded is-dark">
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<span>Demo</span>
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</section>
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<video id="dollyzoom" autoplay controls muted loop playsinline width="80%">
<source src="./static/video/teaser.mp4"
type="video/mp4">
</video>
</div>
<div class="columns is-centered ">
<div class="column is-four-fifths">
One-2-3-45++ is capable of transforming <font color="#3a86ff"> <b>a single RGB image</b> </font> of any object into a high-fidelity textured mesh <font color="#3a86ff"> <b>in under one minute</b></font> . The generated meshes closely mirror the of the original input image. One-2-3-45++ can be <font color="#3a86ff"> <b> trained with only 8 A100 GPUs</b></font>.
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</section>
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<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
Recent advancements in open-world 3D object generation have been remarkable, with image-to-3D methods offering superior fine-grained control over their text-to-3D counterparts. However, most existing models fall short in simultaneously providing rapid generation speeds and high fidelity to input images - two features essential for practical applications. In this paper, we present One-2-3-45++, an innovative method that transforms a single image into a detailed 3D textured mesh in approximately one minute. Our approach aims to fully harness the extensive knowledge embedded in 2D diffusion models and priors from valuable yet limited 3D data. This is achieved by initially fine-tuning a 2D diffusion model for consistent multi-view image generation, followed by elevating these images to 3D with the aid of multi-view conditioned 3D native diffusion models. Extensive experimental evaluations demonstrate that our method can produce high-quality, diverse 3D assets that closely mirror the original input image.
</p>
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</div>
</div>
<!--/ Abstract. -->
</div>
</section>
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<h2 class="title is-3">Method</h2>
</div>
<br>
<img src="./static/figure/method.png"
class=""
alt=""/>
Starting with a single image as input, we initially produce consistent multi-view images by fine-tuning a 2D diffusion model. These multi-view images are then elevated into 3D through a pair of 3D native diffusion networks. Throughout the 3D diffusion process, the generated multi-view images act as essential guiding conditions. After extracting the 3D mesh from the denoised volume, we further enhance the texture by employing a lightweight optimization with multi-view images as supervision. Our One-2-3-45++ is capable of producing an initial textured mesh <font color="#3a86ff"><b>within 20 seconds</b></font> and delivers a refined one in <font color="#3a86ff"><b>roughly one minute </b></font>.
</div>
</div>
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</section>
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<div class = "has-text-centered">
<h2 class="title is-3">More Results (DreamFusion Prompts)</h2>
</div>
<br>
<video id="dollyzoom" autoplay controls muted loop playsinline height="100%">
<source src="./static/video/text.mp4"
type="video/mp4">
</video>
</div>
</div>
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</section>
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<h2 class="title is-3">More Results (GSO dataset)</h2>
</div>
<br>
<video id="dollyzoom" autoplay controls muted loop playsinline height="100%">
<source src="./static/video/gso.mp4"
type="video/mp4">
</video>
</div>
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</section>
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<h2 class="title is-3">More Results (One-2-3-45 test set)</h2>
</div>
<br>
<video id="dollyzoom" autoplay controls muted loop playsinline height="100%">
<source src="./static/video/12345.mp4"
type="video/mp4">
</video>
</div>
</div>
</div>
</section>
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<div class = "has-text-centered">
<h2 class="title is-3">User Study</h2>
</div>
<br>
<img src="./static/figure/user_study.png"
class=""
alt=""/>
Results of a user study involving 53 participants. Each cell displays the probability or preference rate at which one method (row) outperforms another (column).
</div>
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</div>
</section>
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<div class = "has-text-centered">
<h2 class="title is-3">Applications</h2>
</div>
<br>
<video id="dollyzoom" autoplay controls muted loop playsinline height="100%">
<source src="./static/video/aipoly.mp4"
type="video/mp4">
</video>
<p>
One-2-3-45++ can significantly enhance the efficiency and creativity of 3D game artists. Every 3D asset featured in the video was created by our AI.
</p>
</div>
</div>
</div>
</section>
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>
@article{liu2023one2345++,
title={One-2-3-45++: Fast Single Image to 3D Objects with Consistent Multi-View Generation and 3D Diffusion},
author={Minghua Liu and Ruoxi Shi and Linghao Chen and Zhuoyang Zhang and Chao Xu and Xinyue Wei and Hansheng Chen and Chong Zeng and Jiayuan Gu and Hao Su},
journal={arXiv preprint arXiv:2311.07885},
year={2023}
}
</code></pre>
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