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< title > LION: Latent Point Diffusion Models for 3D Shape Generation < / title >
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< a href = "https://www.nvidia.com/" > < img width = "100%" src = "assets/nvidia.svg" > < / a >
< a href = "https://nv-tlabs.github.io/" > < strong > Toronto AI Lab< / strong > < / a >
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< div class = "container" >
< div class = "paper-title" >
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< h1 >
< font color = "#5364cc" > LION< / font > :
< font color = "#5364cc" > L< / font > atent Point Diffus< font color = "#5364cc" > ion< / font > Models < br > for 3D Shape Generation< / h1 >
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< / div >
< div id = "authors" >
< center >
< 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 = "http://latentspace.cc/" > Arash Vahdat< sup > 1< / sup > < / a > ,
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< a href = "https://www.fwilliams.info/" > Francis Williams< sup > 1< / sup > < / a > ,
< a href = "https://zgojcic.github.io/" > Zan Gojcic< sup > 1< / sup > < / a > ,
< a href = "https://orlitany.github.io/" > Or Litany< sup > 1< / sup > < / a > ,
< a href = "https://www.cs.utoronto.ca/~fidler/" > Sanja Fidler< sup > 1,2,3< / sup > < / a > ,
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< a href = "https://karstenkreis.github.io/" > Karsten Kreis< sup > 1< / sup > < / a >
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< / center >
< center >
< div class = "affiliations" >
< 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 / >
< / div >
< div class = "affil-row" >
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< div class = "venue text-center" > < b > NeurIPS 2022 < / b > < / div >
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< / div >
< / center >
< div style = "clear: both" >
< div class = "paper-btn-parent" >
< a class = "paper-btn" href = "https://arxiv.org/abs/2112.07068" >
< span class = "material-icons" > description < / span >
Paper
< / a >
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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 >
Code
< / a >
< / div >
< / div > < / div >
< / div >
< section id = "teaser-image" >
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< center >
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< figure >
< video class = "centered" width = "80%" controls autoplay muted playsinline class = "video-background " >
< source src = "assets/LION_video_v8.mp4#t=0.001" type = "video/mp4" >
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< p class = "caption" >
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LION's Generation process.
< / p > < br >
< / figure >
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< / center >
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< / section >
< section id = "news" >
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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 >
< / section >
< section id = "abstract" / >
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< h2 > Abstract< / h2 >
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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
for digital artists, we require (i) high generation quality, (ii) flexibility for manipulation and applications such as conditional
synthesis and shape interpolation, and (iii) the ability to output smooth surfaces or meshes. To this end, we introduce the
hierarchical Latent Point Diffusion Model (LION) for 3D shape generation. LION is set up as a variational autoencoder (VAE) with
a hierarchical latent space that combines a global shape latent representation with a point-structured latent space. For generation,
we train two hierarchical DDMs in these latent spaces. The hierarchical VAE approach boosts performance compared to DDMs that operate
on point clouds directly, while the point-structured latents are still ideally suited for DDM-based modeling. Experimentally, LION
achieves state-of-the-art generation performance on multiple ShapeNet benchmarks. Furthermore, our VAE framework allows us to easily
use LION for different relevant tasks without re-training the latent DDMs: We show that LION excels at multimodal shape denoising and
voxel-conditioned synthesis. We also demonstrate shape autoencoding and latent shape interpolation, and we augment LION with modern
surface reconstruction techniques to generate smooth 3D meshes. We hope that LION provides a powerful tool for artists working with
3D shapes due to its high-quality generation, flexibility, and surface reconstruction.
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< h2 > Method< / h2 >
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LION is set up as a hierarchical point cloud VAE with denoising diffusion models over the shape latent and latent point distributions.
Point-Voxel CNNs (PVCNN) with adaptive Group Normalization (Ada. GN) are used as neural networks.
The latent points can be interpreted as a smoothed version of the input point cloud.
Shape As Points (SAP) is optionally used for mesh reconstruction.
< / p >
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< figure style = "width: 100%;" >
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< img width = "50%" src = "assets/pipeline.jpg" >
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< p class = "caption" style = "margin-bottom: 24px;" >
Architecture of LION.
< / p >
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< / center >
< / section >
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Generated output from LION.
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< h2 > Technical Contributions< / h2 >
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< div class = "flex-row" >
< p > We make the following technical contributions:
< 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 >
< li > We train latent DDMs in 3D generation.< / li >
< 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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< section id = "results" >
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< h2 > Generation (Single Category)< / h2 >
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< p > Samples from LION trained on single catgory. < / p >
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< center >
< figure >
< video class = "centered" width = "100%" controls autoplay muted playsinline class = "video-background " >
< source src = "assets/gen_airplane.mp4#t=0.001" type = "video/mp4" >
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< p class = "caption" >
Generated point clouds and reconstructed mesh of airplanes.
< / p > < br >
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< source src = "assets/gen_chair.mp4#t=0.001" type = "video/mp4" >
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< p class = "caption" >
Generated point clouds and reconstructed mesh of chair.
< / p > < br >
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< video class = "centered" width = "100%" controls autoplay muted playsinline class = "video-background " >
< source src = "assets/gen_car.mp4#t=0.001" type = "video/mp4" >
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Generated point clouds and reconstructed mesh of car.
< / p > < br >
< / figure >
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< source src = "assets/gen_animal553_v2.mp4#t=0.001" type = "video/mp4" >
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< p class = "caption" style = "margin-bottom: 24px;" >
Generated point clouds and reconstructed mesh of Animal.
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< video class = "centered" width = "100%" controls autoplay muted playsinline class = "video-background " >
< source src = "assets/gen_bottle.mp4#t=0.001" type = "video/mp4" >
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< p class = "caption" >
Generated point clouds and reconstructed mesh of bottle.
< / p > < br >
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< video class = "centered" width = "100%" controls autoplay muted playsinline class = "video-background " >
< source src = "assets/gen_mug.mp4#t=0.001" type = "video/mp4" >
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< p class = "caption" >
Generated point clouds and reconstructed mesh of mug.
< / p > < br >
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< / center >
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< h2 > Generation (Multi-Classes)< / h2 >
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< p > samples from LION trained on multiple ShapeNet catgories, without conditioning. < / p >
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< video class = "centered" width = "100%" controls autoplay muted playsinline class = "video-background " >
< source src = "assets/gen_all_v13.mp4#t=0.001" type = "video/mp4" >
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Generated point clouds and reconstructed mesh. LION model trained on 13 ShapeNet categories jointly without conditioning.
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< source src = "assets/gen_all_55.mp4#t=0.001" type = "video/mp4" >
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Generated point clouds and reconstructed mesh. LION model trained on 55 ShapeNet categories jointly without conditioning.
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< section id = "more_results" >
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< h2 > More Results< / h2 >
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< h3 > Interpolation < / h3 >
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< 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 >
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< figure >
< video class = "centered" width = "100%" controls muted playsinline class = "video-background " >
< source src = "assets/LION_interp.mp4#t=0.001" type = "video/mp4" >
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Left most shape: the source shape. Right most shape: the target shape. The shapes in middle are interpolated results between source and target shape.
< / p >
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< video class = "centered" width = "50%" controls loop autoplay muted playsinline class = "video-background " >
< source src = "assets/LION_interp_seq.mp4#t=0.001" type = "video/mp4" >
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LION traverses the latent space and interpolates many different shapes.
< / p >
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< h3 > Fast Sampling with DDIM < / h3 >
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< p > The sampling time of LION can be reduced by applying DDIM sampler.
DDIM sampler with 25 steps can already generate high-quality shapes, which takes less than 1 sec. < / p >
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< figure style = "width: 100%;" >
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< img width = "100%" src = "assets/ddim_sample.png" >
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< p class = "caption" style = "margin-bottom: 24px;" >
DDIM samples from LION trained on different data. The top two rows show the number of steps and the wall-clock time used when drawing one sample.
With DDIM sampling, we can reduce the sampling time from 27.09 sec (1000 steps) to less than 1 sec (25 steps) to generate an object.
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< h3 > Voxel-Conditioned Synthesis < / h3 >
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< p > Given a coarse voxel grid, LION can generate different plausible detailed shapes. < / p >
< 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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< p class = "caption" style = "margin-bottom: 24px;" width = "30%" >
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Left: Input voxel grid. Right: two point clouds generated by LION and the reconstructed mesh.
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< h3 > Single View Reconstruction < / h3 >
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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’
trained LION’ ’
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
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< img width = "49%" src = "assets/svr/img2shape_mitsuba_full.jpg" >
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Single view reconstruction from RGB images of chair. For each input image, LION can generate multi-modal outputs.
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Single view reconstruction from RGB images of car. For each input image, LION can generate multi-modal outputs.
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More single view reconstruction from RGB images of car.
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< h3 > Text-Guided Generation < / h3 >
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Using CLIP’
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< img width = "35%" src = "assets/clipforge_chair.png" >
< img width = "60%" src = "assets/clipforge_car.png" >
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Text-driven shape generation of chairs with LION. Bottom row is the text input
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< h3 > Per-sample Text-driven Texture Synthesis < / h3 >
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We apply Text2mesh on some generated meshes from LION to additionally synthesize textures in a text-driven manner, leveraging CLIP. The original mesh is generated by LION.
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< img width = "100%" src = "assets/text2mesh/strawberries_airplane-rec_3.jpg" >
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< figcaption align = "center" > An airplane made of strawberry< / figcaption >
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< img width = "100%" src = "assets/text2mesh/fabric_leather_airplane-rec_3.jpg" >
< figcaption align = "center" > An airplane made of fabric leather < / figcaption >
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< img width = "100%" src = "assets/text2mesh/wood_chair-rec_421_norm1.jpg" >
< figcaption align = "center" > A chair made of wood< / figcaption >
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< img width = "100%" src = "assets/text2mesh/wrong_copied1-rec_293_norm0.jpg" >
< figcaption align = "center" > A car made of rusty metal< / figcaption >
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< figcaption align = "center" > A car made of brick< / figcaption >
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< img width = "100%" src = "assets/text2mesh/wrong_copied1-rec_12_norm1.jpg" >
< figcaption align = "center" > A denim fabric animal< / figcaption >
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< h2 > Paper< / h2 >
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< a href = "https://nv-tlabs.github.io/CLD-SGM" > < img class = "screenshot" src = "assets/cld_paper_preview.png" > < / a >
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< 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 >
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< h2 > Citation< / h2 >
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< 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)},
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year={2022}
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