We introduce Text Encoded Extrusions (TEE), a text-based representation that expresses mesh construction as sequences of face extrusions rather than polygon lists, and a method for generating 3D meshes from TEE using a large language model (LLM). By learning extrusion sequences that assemble a mesh, similar to the way artists create meshes, our approach naturally supports arbitrary output face counts and produces manifold meshes by design, in contrast to recent mesh generative transformer based models. The learnt extrusion sequences can also be applied to existing meshes - enabling editing in addition to generation. To train our model, we decompose a library of quadrilateral meshes with non-self-intersecting face loops into constituent loops, which can be viewed as their building blocks, and finetune an LLM on the steps for reassembling the quadrilateral meshes by performing a sequence of extrusions. We demonstrate that our representation enables reconstruction, novel shape synthesis, and the addition of new features to existing meshes.

翻译：我们提出文本编码拉伸（TEE），这是一种基于文本的表达方式，将网格构建表示为一系列面拉伸操作而非多边形列表，并开发了一种利用大型语言模型（LLM）从TEE生成三维网格的方法。通过学习组装网格的拉伸序列（类似于艺术家创建网格的方式），我们的方法自然支持任意数量的输出面，并固有地生成流形网格，这与近期基于变换器的网格生成模型不同。学习的拉伸序列还可应用于现有网格——除生成外还可实现编辑。为训练模型，我们将非自交面循环的四边网格库分解为构成循环（可视为其构建基块），并通过对LLM进行微调，使其学习通过执行一系列拉伸操作重新组装四边网格的步骤。实验证明，我们的表达方式能够实现网格重建、新颖形状合成以及向现有网格添加新特征。