用三角形条带排序生成高质量艺术级网格，兼顾结构与纹理布局。
Strips as Tokens: Artist Mesh Generation with Native UV Segmentation
- 以三角形条带为令牌序列，保持边缘连续性与语义布局。
- 统一表示支持三角/四边形网格联合训练，提升几何规则性。
- 适合需要高精度建模与纹理对齐的3D内容创作人员。
自回归Transformer在生成艺术家级网格方面展现出巨大潜力。然而，现有方法的令牌排序策略难以满足专业标准：基于坐标的排序导致序列过长，基于块的启发式方法破坏了连续边缘流和高质量建模所需的结构规律性。为此，我们提出Strips as Tokens（SATO），一种受三角形条带启发的新框架。通过构建显式编码UV边界、连贯连接面片的序列，该方法自然保留了艺术家创建网格所具有的有序边缘流与语义布局。其关键优势在于统一表示：同一令牌序列可解码为三角形或四边形网格。这种灵活性支持在两种数据上联合训练——大规模三角形数据提供基础结构先验，高质量四边形数据增强输出的几何规则性。大量实验表明，SATO在几何质量、结构一致性及UV分割方面均持续优于现有方法。
原文摘要 · Abstract (English)
Recent advancements in autoregressive transformers have demonstrated remarkable potential for generating artist-quality meshes. However, the token ordering strategies employed by existing methods typically fail to meet professional artist standards, where coordinate-based sorting yields inefficiently long sequences, and patch-based heuristics disrupt the continuous edge flow and structural regularity essential for high-quality modeling. To address these limitations, we propose Strips as Tokens (SATO), a novel framework with a token ordering strategy inspired by triangle strips. By constructing the sequence as a connected chain of faces that explicitly encodes UV boundaries, our method naturally preserves the organized edge flow and semantic layout characteristic of artist-created meshes. A key advantage of this formulation is its unified representation, enabling the same token sequence to be decoded into either a triangle or quadrilateral mesh. This flexibility facilitates joint training on both data types: large-scale triangle data provides fundamental structural priors, while high-quality quad data enhances the geometric regularity of the outputs. Extensive experiments demonstrate that SATO consistently outperforms prior methods in terms of geometric quality, structural coherence, and UV segmentation. Project page: https://ruixu.me/html/SATO/index.html
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