672 lines
22 KiB
HTML
672 lines
22 KiB
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<script type="text/javascript" src="../js/hidebib.js"></script>
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<link href='https://fonts.googleapis.com/css?family=Titillium+Web:400,600,400italic,600italic,300,300italic' rel='stylesheet' type='text/css'>
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<head>
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<title> LION: Latent Point Diffusion Models for 3D Shape Generation </title>
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<meta name="viewport" content="width=device-width, initial-scale=1">
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<!-- meta property="og:description" content="Score-Based Generative Modeling with Critically-Damped Langevin Diffusion"/ -->
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<link href="https://fonts.googleapis.com/css2?family=Material+Icons" rel="stylesheet">
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<!-- meta name="twitter:card" content="summary_large_image" -->
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<!-- meta name="twitter:creator" content="@timudk" -->
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<!-- meta name="twitter:title" content="Score-Based Generative Modeling with Critically-Damped Langevin Diffusion" -->
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<!-- meta name="twitter:description" content="Inspired by connections to statistical mechanics, we propose a novel diffusion process, critically-damped Langevin diffusion, that perturbs the data in a smoother manner by leveraging auxiliary velocity variables. This allows us to denoise more efficiently and learn higher quality generative models." -->
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<!-- meta name="twitter:image" content="https://nv-tlabs.github.io/CLD-SGM/assets/cld_teaser_resized.png" -->
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</head>
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<body>
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<div class="topnav" id="myTopnav">
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<div>
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<a href="https://www.nvidia.com/"><img width="100%" src="assets/nvidia.svg"></a>
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<a href="https://nv-tlabs.github.io/" ><strong>Toronto AI Lab</strong></a>
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</div>
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</div>
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<div class="container">
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<div class="paper-title">
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<h1> LION: Latent Point Diffusion Models <br> for 3D Shape Generation</h1>
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</div>
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<div id="authors">
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<center>
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<div class="author-row-new">
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<a href="https://www.cs.utoronto.ca/~xiaohui/">Xiaohui Zeng<sup>1,2,3</sup></a>,
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<a href="https://www.fwilliams.info/">Francis Williams<sup>1</sup></a>,
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<a href="https://zgojcic.github.io/">Zan Gojcic<sup>1</sup></a>,
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<a href="https://orlitany.github.io/">Or Litany<sup>1</sup></a>,
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<a href="https://www.cs.utoronto.ca/~fidler/">Sanja Fidler<sup>1,2,3</sup></a>,
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<a href="http://latentspace.cc/">Arash Vahdat<sup>1</sup></a>,
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<a href="https://karstenkreis.github.io/">Karsten Kreis<sup>1</sup></a>
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</div>
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</center>
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<center>
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<div class="affiliations">
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<span><sup>1</sup> NVIDIA</span>
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<span><sup>2</sup> University of Toronto</span>
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<span><sup>3</sup> Vector Institute</span> <br/>
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</div>
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<div class="affil-row">
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<div class="venue text-center"><b>NeurIPS 2022 </b></div>
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</div>
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</center>
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<div style="clear: both">
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<div class="paper-btn-parent">
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<a class="paper-btn" href="https://arxiv.org/abs/2112.07068">
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<span class="material-icons"> description </span>
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Paper
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</a>
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<div class="paper-btn-coming-soon">
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<a class="paper-btn" href="https://github.com/nv-tlabs/LION">
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<span class="material-icons"> code </span>
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Code
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</a>
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</div>
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</div></div>
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</div>
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<br>
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<section id="teaser-image">
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<center>
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</p><figure style="margin-top: 20px; margin-bottom: 20px;">
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<img width="50%" src="./assets/lion_teaser.png" style="margin-bottom: 20px;">
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<p class="caption">
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caption for the teaser
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</p><p class="caption">
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</p>
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</center>
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</section>
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<section id="news">
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<hr>
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<h2>News</h2>
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<div class="row">
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<div><span class="material-icons"> event </span> [Sept 2022] Build the project page <a href="https://github.com/nv-tlabs/LION">Page</a>!</div>
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</div>
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</section>
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<section id="abstract"/>
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<hr>
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<h2>Abstract</h2>
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<div class="flex-row">
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<p>
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Denoising diffusion models (DDMs) have shown promising results in 3D point cloud synthesis. To advance 3D DDMs and make them useful
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for digital artists, we require (i) high generation quality, (ii) flexibility for manipulation and applications such as conditional
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synthesis and shape interpolation, and (iii) the ability to output smooth surfaces or meshes. To this end, we introduce the
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hierarchical Latent Point Diffusion Model (LION) for 3D shape generation. LION is set up as a variational autoencoder (VAE) with
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a hierarchical latent space that combines a global shape latent representation with a point-structured latent space. For generation,
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we train two hierarchical DDMs in these latent spaces. The hierarchical VAE approach boosts performance compared to DDMs that operate
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on point clouds directly, while the point-structured latents are still ideally suited for DDM-based modeling. Experimentally, LION
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achieves state-of-the-art generation performance on multiple ShapeNet benchmarks. Furthermore, our VAE framework allows us to easily
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use LION for different relevant tasks without re-training the latent DDMs: We show that LION excels at multimodal shape denoising and
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voxel-conditioned synthesis. We also demonstrate shape autoencoding and latent shape interpolation, and we augment LION with modern
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surface reconstruction techniques to generate smooth 3D meshes. We hope that LION provides a powerful tool for artists working with
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3D shapes due to its high-quality generation, flexibility, and surface reconstruction.
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</p>
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</div>
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</section>
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<section id="teaser-video">
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</p>
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<center>
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<figure>
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<video class="centered" width="50%" controls muted autoplay>
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<source src="assets/LION_demo.mp4#t=0.001" type="video/mp4">
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Your browser does not support the video tag.
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</video>
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<p class="caption">
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Generated output from LION.
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</p>
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</figure>
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</center>
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</p>
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</section>
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<section id="novelties"/>
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<hr>
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<h2>Technical Contributions</h2>
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<div class="flex-row">
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<p>We make the following technical contributions:
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<ul style="list-style-type:disc;">
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<li>We explore the training of multiple denoising diffusion models (DDMs) in a latent space..</li>
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<li>We train latent DDMs in 3D generation.</li>
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<li>We outperform all baselines and demonstrate that LION scale to extremely diverse shape datasets, like modeling 13 or even 55 ShapeNet categories jointly without conditioning. </li>
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</ul>
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</p>
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</div>
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</section>
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<section id="results">
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<hr>
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<h2>Generation (Single Category)</h2>
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<div class="flex-row">
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<p>Samples from LION trained on single catgory. </p>
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</div>
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<center>
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<figure>
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<video class="centered" width="100%" controls autoplay muted playsinline class="video-background " >
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<source src="assets/gen_airplane.mp4#t=0.001" type="video/mp4">
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Your browser does not support the video tag.
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</video>
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<p class="caption">
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Generated point clouds and reconstructed mesh of airplanes.
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</p> <br>
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</figure>
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<figure>
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<video class="centered" width="100%" controls autoplay muted playsinline class="video-background " >
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<source src="assets/gen_chair.mp4#t=0.001" type="video/mp4">
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Your browser does not support the video tag.
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</video>
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<p class="caption">
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Generated point clouds and reconstructed mesh of chair.
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</p> <br>
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</figure>
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<figure>
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<video class="centered" width="100%" controls autoplay muted playsinline class="video-background " >
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<source src="assets/gen_car.mp4#t=0.001" type="video/mp4">
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Your browser does not support the video tag.
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</video>
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<p class="caption">
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Generated point clouds and reconstructed mesh of car.
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</p> <br>
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</figure>
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<figure style="width: 100%;">
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<video class="centered" width="100%" controls autoplay muted playsinline class="video-background " >
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<source src="assets/gen_animal.mp4#t=0.001" type="video/mp4">
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Your browser does not support the video tag.
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</video>
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<p class="caption" style="margin-bottom: 24px;">
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Generated point clouds and reconstructed mesh of Animal.
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</p> <br>
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</figure>
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</center>
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<hr>
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<h2>Generation (Multi-Classes)</h2>
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<!-- <div class="flex-row">
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<p>samples from LION trained on multiple ShapeNet catgories, without conditioning. </p>
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</div> -->
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<center>
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<figure>
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<video class="centered" width="100%" controls autoplay muted playsinline class="video-background " >
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<source src="assets/gen_all_v13.mp4#t=0.001" type="video/mp4">
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Your browser does not support the video tag.
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</video>
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<p class="caption">
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Generated point clouds and reconstructed mesh. LION model trained on 13 ShapeNet categories jointly without conditioning.
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</p>
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<br>
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</figure>
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</center>
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</section>
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<section id="more_results">
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<hr>
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<h2>Applications</h2>
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<h3>Voxel-Conditioned Synthesis </h3>
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<div class="flex-row">
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<p>Given a coarse voxel grid, LION can generate different plausible detailed shapes. </p>
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<p>In practice, an artist using a 3D generative model may have a rough idea of the desired shape. For instance, they may be able to quickly construct a coarse voxelized shape, to which the generative model then adds realistic details. </p>
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</div>
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<center>
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<figure style="width: 40%;">
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<video class="centered" width="30%" controls autoplay muted playsinline class="video-background " >
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<source src="assets/airplane_voxel_input_165.mp4#t=0.01" type="video/mp4">
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Your browser does not support the video tag.
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</video>
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<p class="caption" style="margin-bottom: 24px;" width="30%">
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Input voxel grid
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<!-- Voxel-guided synthesis experiments, on different categories. We run diffuse-denoise in latent space to generate diverse plausible clean shapes (first row, left plane: 250 diffuse-denoise steps; first row, right plane: 200 steps;) -->
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</p>
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</figure>
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<figure style="width: 40%;">
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<video class="centered" width="30%" controls autoplay muted playsinline class="video-background " >
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||
<source src="assets/airplane_voxel_output_165_D200_1.mp4#t=0.001" type="video/mp4">
|
||
Your browser does not support the video tag.
|
||
</video>
|
||
<p class="caption" style="margin-bottom: 24px;" width="30%">
|
||
Output point clouds and mesh.
|
||
</p>
|
||
|
||
</figure>
|
||
|
||
</center>
|
||
<br>
|
||
|
||
<h3>Interpolation </h3>
|
||
<div class="flex-row">
|
||
<p>LION can interpolate two shapes by traversing the latent space. The generated shapes are clean and semantically plausible along the entire interpolation path. </p>
|
||
</div>
|
||
<figure>
|
||
<video class="centered" width="40%" autoplay loop muted playsinline class="video-background " >
|
||
<source src="assets/LION_latent_interp.mp4#t=0.001" type="video/mp4">
|
||
Your browser does not support the video tag.
|
||
</video>
|
||
<!-- <p class="caption">
|
||
The sequence above is generated by interpolating the latent variable of the source and target shapes (using the probability flow ODE formulation).
|
||
</p> -->
|
||
</figure>
|
||
<br>
|
||
|
||
<h3> Per-sample Text-driven Texture Synthesis </h3>
|
||
<div class="flex-row">
|
||
<p>
|
||
We apply Text2mesh on some generated meshes from LION to additionally synthesize textures in a text-driven manner, leveraging CLIP.
|
||
</p>
|
||
</div>
|
||
<figure style="width: 100%;">
|
||
<a>
|
||
<img width="100%" src="assets/text2mesh_airplane.png">
|
||
</a>
|
||
<p class="caption" style="margin-bottom: 24px;">
|
||
Text2mesh results with text prompt "a airplane made of __". All prompts are applied on the same generated shapes. The original mesh is generated by LION.
|
||
</p>
|
||
</figure>
|
||
<br>
|
||
|
||
<h3> Single View Reconstruction </h3>
|
||
<div class="flex-row">
|
||
<p>
|
||
We extend LION to also allow for single view reconstruction (SVR) from RGB data. We render 2D
|
||
images from the 3D ShapeNet shapes, extracted the images’ CLIP image embeddings, and
|
||
trained LION’s latent diffusion models while conditioning on the shapes’ CLIP image embeddings.
|
||
At test time, we then take a single view 2D image, extract the CLIP image embedding, and generate
|
||
corresponding 3D shapes, thereby effectively performing SVR. We show SVR results from real
|
||
RGB data
|
||
</p>
|
||
</div>
|
||
<figure style="width: 50%;">
|
||
<a>
|
||
<img width="100%" src="assets/svr_chair.png">
|
||
</a>
|
||
<p class="caption" style="margin-bottom: 24px;">
|
||
Single view reconstruction from RGB images of chair. For each input image, LION can generate multi-modal outputs.
|
||
</p>
|
||
</figure>
|
||
<figure style="width: 50%;">
|
||
<a>
|
||
<img width="100%" src="assets/svr_car.png">
|
||
</a>
|
||
<p class="caption" style="margin-bottom: 24px;">
|
||
Single view reconstruction from RGB images of car. For each input image, LION can generate multi-modal outputs.
|
||
</p>
|
||
</figure>
|
||
<figure style="width: 100%;">
|
||
<a>
|
||
<img width="100%" src="assets/svr_car_more.png">
|
||
</a>
|
||
<p class="caption" style="margin-bottom: 24px;">
|
||
More single view reconstruction from RGB images of car.
|
||
</p>
|
||
</figure>
|
||
<br>
|
||
<h3> Text-Guided Generation </h3>
|
||
<div class="flex-row">
|
||
<p>
|
||
Using CLIP’s text encoder, our method additionally allows for text-guided generation.
|
||
</p>
|
||
</div>
|
||
<figure style="width: 50%;">
|
||
<a>
|
||
<img width="100%" src="assets/clipforge_chair.png">
|
||
</a>
|
||
<p class="caption" style="margin-bottom: 24px;">
|
||
Text-driven shape generation of chairs with LION. Bottom row is the text input
|
||
</p>
|
||
</figure>
|
||
<figure style="width: 50%;">
|
||
<a>
|
||
<img width="100%" src="assets/clipforge_car.png">
|
||
</a>
|
||
<p class="caption" style="margin-bottom: 24px;">
|
||
Text-driven shape generation of cars with LION. Bottom row is the text input
|
||
</p>
|
||
</figure>
|
||
|
||
|
||
</section>
|
||
<section id="paper">
|
||
<h2>Paper</h2>
|
||
<hr>
|
||
<div class="flex-row">
|
||
<div class="download-thumb">
|
||
<div style="box-sizing: border-box; padding: 16px; margin: auto;">
|
||
<a href="https://nv-tlabs.github.io/CLD-SGM"><img class="screenshot" src="assets/cld_paper_preview.png"></a>
|
||
</div>
|
||
</div>
|
||
<div class="paper-stuff">
|
||
<p><b>LION: Latent Point Diffusion Models for 3D Shape Generation</b></p>
|
||
<p>Xiaohui Zeng, Arash Vahdat, Francis Williams, Zan Gojcic, Or Litany, Sanja Fidler, Karsten Kreis</p>
|
||
<p><i>Advances in Neural Information Processing Systems (NeurIPS), 2022 <b></b></i></p>
|
||
<!--
|
||
<div><span class="material-icons"> description </span><a href="https://arxiv.org/abs/2112.07068"> arXiv version</a></div>
|
||
<div><span class="material-icons"> insert_comment </span><a href="assets/dockhorn2021score.bib"> BibTeX</a></div>
|
||
<div><span class="material-icons"> integration_instructions </span><a href="https://github.com/nv-tlabs/CLD-SGM"> Code</a></div>
|
||
-->
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</section>
|
||
|
||
<section id="bibtex">
|
||
<h2>Citation</h2>
|
||
<hr>
|
||
<pre><code>@inproceedings{
|
||
zeng2022lion,
|
||
title={ LION: Latent Point Diffusion Models for 3D Shape Generation },
|
||
author={ Xiaohui Zeng and Arash Vahdat and Francis Williams and Zan Gojcic and Or Litany and Sanja Fidler and Karsten Kreis },
|
||
booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
|
||
year={2022}
|
||
}</code></pre>
|
||
</section>
|
||
</div>
|
||
</body>
|
||
</html>
|