  Probabilistic denoising diffusion models (DDMs) have set a new standard for
2D image generation. Extending DDMs for 3D content creation is an active field
of research. Here, we propose TetraDiffusion, a diffusion model that operates
on a tetrahedral partitioning of 3D space to enable efficient, high-resolution
3D shape generation. Our model introduces operators for convolution and
transpose convolution that act directly on the tetrahedral partition, and
seamlessly includes additional attributes such as color. Remarkably,
TetraDiffusion enables rapid sampling of detailed 3D objects in nearly
real-time with unprecedented resolution. It's also adaptable for generating 3D
shapes conditioned on 2D images. Compared to existing 3D mesh diffusion
techniques, our method is up to 200 times faster in inference speed, works on
standard consumer hardware, and delivers superior results.
