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）已成为二维图像生成的新标杆。将DDMs扩展至三维内容生成是当前活跃的研究领域。本文提出TetraDiffusion，一种在三维空间四面体剖分上运行的扩散模型，可实现高效、高分辨率的三维形状生成。该模型引入了直接在四面体剖分上进行卷积和转置卷积的算子，并能无缝整合颜色等附加属性。值得注意的是，TetraDiffusion能以近乎实时的速度快速采样生成细节丰富的三维物体，且达到前所未有的分辨率。该模型还可适配于基于二维图像条件的三维形状生成。与现有的三维网格扩散技术相比，本方法在推理速度上提升高达200倍，可在标准消费级硬件上运行，并产生更优的结果。