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.

翻译：概率去噪扩散模型（DDMs）已为二维图像生成树立了新标准。将DDMs扩展至三维内容创作是当前活跃的研究领域。本文提出TetraDiffusion——一种基于三维空间四面体剖分的扩散模型，可实现高效高分辨率的三维形状生成。该模型引入了直接作用于四面体剖分的卷积与转置卷积算子，并能无缝集成颜色等附加属性。值得注意的是，TetraDiffusion能以近乎实时的速度快速采样细节丰富的三维对象，且分辨率达到前所未有的水平。此外，该模型还可适用于基于二维图像的三维形状条件生成。相比现有三维网格扩散技术，本方法推理速度提升达200倍，可在标准消费级硬件上运行，并生成更优结果。