条带即令牌：基于原生UV分割的艺术家网格生成

摘要

近期自回归变换器的研究进展显示出生成艺术家级别网格模型的巨大潜力。然而，现有方法采用的标记排序策略通常难以达到专业艺术家的标准——基于坐标的排序会产生低效的长序列，而基于分块的启发式方法会破坏高质量建模所必需的连续边流和结构规整性。为突破这些局限，我们提出条带标记化（SATO）框架，其灵感来源于三角形条带的标记排序策略。通过将序列构建为显式编码UV边界的面连接链，我们的方法天然保留了艺术家创作网格特有的有序边流与语义布局。该方案的关键优势在于其统一表征能力，使得同一标记序列可解码为三角形或四边形网格。这种灵活性实现了对两类数据的联合训练：大规模三角数据提供基础结构先验，而高质量四边数据则增强输出的几何规整性。大量实验表明，SATO在几何质量、结构连贯性和UV分割方面均优于现有方法。

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.

条带即令牌：基于原生UV分割的艺术家网格生成

Strips as Tokens: Artist Mesh Generation with Native UV Segmentation

摘要

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