LION: Latent Point Diffusion Models
for 3D Shape Generation

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.

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.

We make the following technical contributions:

• We explore the training of multiple denoising diffusion models (DDMs) in a latent space..
• We train latent DDMs in 3D generation.
• 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.

Samples from LION trained on single catgory.

LION can interpolate two shapes by traversing the latent space. The generated shapes are clean and semantically plausible along the entire interpolation path.

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.

Given a coarse voxel grid, LION can generate different plausible detailed shapes.

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.

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

Using CLIP’s text encoder, our method additionally allows for text-guided generation.

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.