We introduce Text Encoded Extrusion (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 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 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：本文提出文本编码挤出（TEE）表示法，该表示法将网格构建表达为面挤出序列而非多边形列表，并介绍了一种利用大语言模型（LLM）从TEE生成三维网格的方法。通过学习组装网格的挤出序列（类似于艺术家创建网格的方式），我们的方法天然支持任意输出面数，并通过设计生成流形网格，这与近期基于Transformer的模型形成对比。习得的挤出序列还可应用于现有网格——在生成功能之外实现编辑功能。为训练模型，我们将具有非自相交面环的四边形网格库分解为构成环（可视为其构建单元），并通过微调LLM学习通过执行挤出序列重新组装网格的步骤。实验表明，我们的表示法能够实现网格重建、新形状合成以及对现有网格添加新特征的功能。