用树状序列生成高质量艺术网格，细节更精细、法向更一致
TreeMeshGPT: Artistic Mesh Generation with Autoregressive Tree Sequencing
- 采用动态生长的树结构递推生成三角面，局部扩展提升训练稳定性
- 每面用两个标记，压缩率约22%，支持高细节网格生成
- 显著减少法向翻转问题，适合需精准几何的艺术创作场景
我们提出TreeMeshGPT，一种基于自回归Transformer的艺术网格生成模型，可与输入点云对齐。不同于传统Transformer的下一个词预测，我们引入新型自回归树序列机制：每一步从基于网格三角面邻接关系动态构建的树结构中检索下一个输入标记。该序列方式使网格在每步从最后一个生成的三角面局部扩展，降低训练难度并提升网格质量。每个三角面以两个标记表示，相比原始面标记化实现约22%的压缩率。这种高效标记化使模型能生成高度细节化的艺术网格，并强条件于点云，性能超越以往方法。此外，本方法生成网格具有强法向方向约束，显著减少常见于此前方法的法向翻转问题。实验表明，TreeMeshGPT在细节精炼度和法向一致性方面均明显提升。
原文摘要 · Abstract (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.
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