用文字描述拉伸过程生成3D网格，支持任意面数且可编辑。
Learning to Build Shapes by Extrusion
- 将建模过程拆解为一系列面拉伸操作，用文本序列表示网格构造。
- 能重建、生成新形状并为已有网格添加特征，生成的网格始终是流形的。
- 适合需要灵活生成和修改3D模型的设计师或开发者使用。
我们提出文本编码拉伸（TEE），一种以文本序列形式表达网格构建过程的新方法，通过一系列面拉伸操作而非多边形列表来构建3D网格，并利用大型语言模型（LLM）从TEE生成3D网格。该方法模仿艺术家建模过程，自然支持任意输出面数，且生成的网格天生为流形结构，区别于基于Transformer的近期网格生成模型。通过将非自交的四边形网格库分解为基本面环作为构建模块，我们在这些重构步骤上微调一个LLM，使其学会通过一系列拉伸操作重新组合网格。实验表明，该表示可实现网格重建、新颖形状合成以及在现有网格上添加新特征。
原文摘要 · Abstract (English)
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.
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