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<!DOCTYPE html>
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<h1 class="title is-1 publication-title rainbow-text">DiverseDream</h1>
<h1 class="title is-2 publication-title">Diverse Text-to-3D Synthesis <br> with Augmented Text Embedding</h1>
<h1 class="title is-4 publication-title">ECCV 2024</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://scholar.google.com/citations?user=OpPFGrIAAAAJ&hl=en">Uy Dieu Tran</a><sup>1,*</sup>,</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=Q7C_mlIAAAAJ&hl=en">Minh Luu</a><sup>1,*</sup>,</span>
<span class="author-block">
<a href="https://phongnhhn.info/">Phong Ha Nguyen</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://www.khoinguyen.org/">Khoi Nguyen</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://sonhua.github.io/">Binh-Son Hua</a><sup>1,2</sup>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>VinAI Research,</span>
<span class="author-block"><sup>2</sup>Trinity College Dublin</span>
</div>
<div class="is-size-6 publication-authors">
<span class="author-block"><sup>*</sup>Equal contribution</span>
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<span>arXiv</span>
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<h2 class="subtitle has-text-centered">
<span class="dnerf">DiverseDream</span> can generated diverse 3D object given the same text prompt.
</h2>
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</div>
</section>
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<h2 class="title is-3">Abstract</h2>
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<p>
Text-to-3D synthesis has recently emerged as a new approach to sampling 3D models by adopting pretrained text-to-image models as guiding visual priors. An intriguing but underexplored problem with existing text-to-3D methods is that 3D models obtained from the sampling-by-optimization procedure tend to have mode collapses, and hence poor diversity in their results. In this paper, we provide an analysis and identify potential causes of such a limited diversity, which motivates us to devise a new method that considers the joint generation of different 3D models from the same text prompt. We propose to use augmented text prompts via textual inversion of reference images to diversify the joint generation. We show that our method leads to improved diversity in text-to-3D synthesis qualitatively and quantitatively.
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<h2 class="title is-3">Pipeline Method</h2>
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<p>
We translate the diversity of augmented text prompts to the resulting 3D models via a two-stage method.
<br>
<b>Stage 1: HiPer tokens inversion</b> (left): for each reference image, we seek to learn a HiPer token $h_i$ so that the prompt $[y; h_i]$ reconstructs the reference image.
<br>
<b>Stage 2: Textual score distillation</b> (right): we run a multi-particle variational inference for optimizing the 3D models from text prompt $y$. For each iteration in the optimization, we randomly sample a particle $\theta_i$ with its rendered image $x_i$. We use the augmented text prompt $y'_i = [y; h^*_i;\phi]$, with $\phi$ as shared embedding to condition the optimization of $\theta_i$.
These two are taken as input to a pretrained Stable Diffusion to compute the TSD loss and MSE loss to update the NeRF and shared learnable token $\phi$ iteratively.
</p>
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<h2 class="title is-centered is-3 has-text-centered">Comparision</h2>
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<section class="section" id="BibTeX">
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<h2 class="title">BibTeX</h2>
<pre><code>@inproceedings{tran2025diverse,
title={Diverse Text-to-3D Synthesis with Augmented Text Embedding},
author={Tran, Uy Dieu and Luu, Minh and Nguyen, Phong Ha and Nguyen, Khoi and Hua, Binh-Son},
booktitle={European Conference on Computer Vision},
pages={217--235},
year={2025},
organization={Springer}
}
</code></pre>
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</section>