TreeMeshGPT：基于自回归树序列的艺术化网格生成

TreeMeshGPT: Artistic Mesh Generation with Autoregressive Tree Sequencing

March 14, 2025

作者: Stefan Lionar, Jiabin Liang, Gim Hee Lee

cs.AI

摘要

我们推出了TreeMeshGPT，这是一种自回归Transformer模型，旨在生成与输入点云对齐的高质量艺术网格。不同于传统自回归Transformer中的下一令牌预测，我们提出了一种新颖的自回归树序列生成方法，其中下一输入令牌是从一个动态增长的树结构中检索而来，该树结构基于网格内三角形面的邻接关系构建。我们的序列生成方法使得网格能够在每一步从最后生成的三角形面局部扩展，从而降低了训练难度并提升了网格质量。我们的方法通过两个令牌表示每个三角形面，与朴素的面令牌化相比，实现了约22%的压缩率。这种高效的令牌化使我们的模型能够生成具有强烈点云约束的高度细节化艺术网格，在容量和保真度上均超越了以往方法。此外，我们的方法生成的网格具有严格的法线方向约束，最大限度地减少了以往方法中常见的法线翻转问题。实验表明，TreeMeshGPT通过精细的细节和法线方向一致性显著提升了网格生成的质量。

English

We introduce TreeMeshGPT, an autoregressive Transformer designed to generate high-quality artistic meshes aligned with input point clouds. Instead of the conventional next-token prediction in autoregressive Transformer, we propose a novel Autoregressive Tree Sequencing where the next input token is retrieved from a dynamically growing tree structure that is built upon the triangle adjacency of faces within the mesh. Our sequencing enables the mesh to extend locally from the last generated triangular face at each step, and therefore reduces training difficulty and improves mesh quality. Our approach represents each triangular face with two tokens, achieving a compression rate of approximately 22% compared to the naive face tokenization. This efficient tokenization enables our model to generate highly detailed artistic meshes with strong point cloud conditioning, surpassing previous methods in both capacity and fidelity. Furthermore, our method generates mesh with strong normal orientation constraints, minimizing flipped normals commonly encountered in previous methods. Our experiments show that TreeMeshGPT enhances the mesh generation quality with refined details and normal orientation consistency.