Strips as Tokens: Artist Mesh Generation with Native UV Segmentation
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.
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Rui Xu
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2026-04-14T08:17:28.507Z
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Rui Xu
Xrvitd
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Xrvitd
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Dafei Qin
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Kaichun Qiao
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2026-04-14T08:17:24.909Z
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Kaichun Qiao
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DolphinQiao
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Qiujie Dong
claimed_verified
2026-04-14T08:17:26.728Z
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Qiujie Dong
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QiujieDong
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Huaijin Pi
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Qixuan Zhang
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Longwen Zhang
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Lan Xu
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Jingyi Yu
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Wenping Wang
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Taku Komura
Strips as Tokens: Artist Mesh Generation with Native UV Segmentation
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.
SATO introduces a novel token ordering strategy for autoregressive transformers that preserves edge flow and semantic layout in mesh generation through triangle strip-based sequences.
autoregressive transformers token ordering strategies triangle strips UV boundaries mesh generation geometric quality structural coherence UV segmentation