  Diffusion models have shown great promise for image generation, beating GANs
in terms of generation diversity, with comparable image quality. However, their
application to 3D shapes has been limited to point or voxel representations
that can in practice not accurately represent a 3D surface. We propose a
diffusion model for neural implicit representations of 3D shapes that operates
in the latent space of an auto-decoder. This allows us to generate diverse and
high quality 3D surfaces. We additionally show that we can condition our model
on images or text to enable image-to-3D generation and text-to-3D generation
using CLIP embeddings. Furthermore, adding noise to the latent codes of
existing shapes allows us to explore shape variations.
