Strips as Tokens: Artist Mesh Generation with Native UV Segmentation

TL;DR

SATO introduces a novel token ordering strategy inspired by triangle strips for mesh generation, improving geometric quality and UV segmentation by preserving edge flow and enabling joint training on triangle and quad data.

Contribution

It proposes a unified token sequence encoding mesh faces with UV boundaries, enhancing mesh quality and structural coherence over prior methods.

Findings

Outperforms previous methods in geometric quality and structure.

Enables joint training on triangle and quadrilateral mesh data.

Improves UV segmentation and edge flow preservation.

Abstract

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