Strips as Tokens: Artist Mesh Generation with Native UV Segmentation

Rui Xu; Dafei Qin; Kaichun Qiao; Qiujie Dong; Huaijin Pi; Qixuan Zhang; Longwen Zhang; Lan Xu

Figure 1. Strips as Tokens (SATO) enables unified, high-quality artist mesh generation with native UV segmentation. Our strip-based tokenizer supports both triangle (left) and quad (right) meshes without retraining and automatically segments UV charts (side) during autoregressive generation.

Figure 2. Artist meshes differ markedly from geometry-processed ones. Here we show quadrilateral and triangular meshes constructed by artists, as well as meshes created using geometric processing methods (such as Delaunay-style remeshing ( Xu et al., 2024 ) and Marching Cubes ( Lorensen and Cline, 1998 ) ).

Figure 3

Figure 3. The Pipeline of SATO . SATO uses a strip-based tokenizer to encode/decode both triangle and quad meshes as a unified discrete sequence. Conditioned on an input point cloud, a learnable point-cloud encoder cross-attends to the core Hourglass Transformer, which autoregressively generates token sequences that are decoded into triangle or quad meshes with native UV segmentation.

Figure 5

Figure 6

Figure 4. Mesh tokenization with prefix sharing. We use a three-level hierarchical coordinate

(c_{1},c_{2},c_{3})

with prefix sharing to compress token sequences following DeepMesh ( Zhao et al., 2025 ) , repeated prefixes between consecutive vertices are omitted. To mark a strip transition, we introduce a special top-level token vocabulary

c_{1}^{*}

(red), which is distinct from

c_{1}

but serves the same role. Whenever

c_{1}^{*}

appears, the prefix sharing state is reset, starting a new prefix context.

Figure 5. Artist-created meshes with UV chart partitions. We split artist meshes into UV parts and let SATO traverse all triangles within one part before a UV segmentation transition to the next part, enabling native UV segmentation during generation.

Figure 6. Unified representation of triangle and quad using strips. Triangle strips may locally “turn” under edge flips (a). In contrast, quad strips avoid this ambiguity (b), as each step admits only a single forward direction. Moreover, sequences tokenized from a quad mesh can be decoded into triangles while still preserving high quality (c). Note that the quad token sequence of (b) is totally the same as the triangle token sequence of (c).

Metrics	BPT	DeepMesh	SATO
Vocab Size $\downarrow$	40960	4736	4800
Comp Rate $\downarrow$	0.228	0.330	0.283

Table 1. Comparison of vocabulary size and average compression rate. The compression rate is computed as the token sequence length divided by (face count × 9).

Figure 7. Different face ordering defined by other methods. BPT ( Weng et al., 2025 ) and DeepMesh ( Zhao et al., 2025 ) traverse local fan-/disk-shaped neighborhoods, i.e., triangles rotate around a vertex, which triggers patch transitions more frequently. In contrast, our strip-based ordering can, in principle, extend arbitrarily long.

Figure 8. The gallery of SATO illustrates our model’s outputs across three tasks. From bottom to top, it shows triangular mesh generation, shape generation with UV segmentation, and quadrilateral mesh generation. SATO supports all three tasks within a single framework and achieves compelling results on each of them.

	ShapeNet				Thingi10K				Objaverse
Method	$\mathrm{NC}\uparrow$	$\mathrm{CD}\downarrow$	$\mathrm{HD}\downarrow$	$\mathrm{F1}\uparrow$	$\mathrm{NC}\uparrow$	$\mathrm{CD}\downarrow$	$\mathrm{HD}\downarrow$	$\mathrm{F1}\uparrow$	$\mathrm{NC}\uparrow$	$\mathrm{CD}\downarrow$	$\mathrm{HD}\downarrow$	$\mathrm{F1}\uparrow$
MeshAnythingV2 ( Chen et al., 2024c )	0.911	0.009	0.078	0.361	0.841	0.022	0.168	0.162	0.858	0.016	0.117	0.208
TreeMeshGPT ( Lionar et al., 2025 )	0.840	0.034	0.161	0.439	0.791	0.058	0.228	0.236	0.783	0.057	0.238	0.188
BPT ( Weng et al., 2025 )	0.962	0.003	0.017	0.605	0.874	0.028	0.141	0.248	0.841	0.030	0.137	0.265
DeepMesh ( Zhao et al., 2025 )	0.967	0.004	0.037	0.532	0.853	0.026	0.167	0.157	0.859	0.020	0.120	0.240
SATO	0.975	0.002	0.032	0.807	0.916	0.009	0.154	0.460	0.909	0.009	0.117	0.503

Table 2. Quantitative comparison on ShapeNet ( Chang et al., 2015 ) , Thingi10K ( Zhou and Jacobson, 2016 ) , and Objaverse ( Deitke et al., 2023 ) datasets. The best scores are emphasized in bold with underlining, while the second best scores are highlighted only in bold.

Figure 9. Overview of our test dataset. 250 models selected from the ShapeNet ( Chang et al., 2015 ) , Thingi10K ( Zhou and Jacobson, 2016 ) and Objaverse ( Deitke et al., 2023 ) dataset.

Figure 10. Qualitative comparison with baseline methods across different shapes. Our approach consistently produces high-quality artist meshes with stable structure and clean surface.

	MeshAthV2	TreeMeshGPT	BPT	DeepMesh	Ours
Scores	0.18	0.57	1.4	1.17	2.61
Variance	0.27	0.67	0.95	0.93	0.49

Table 3. User study with SOTA methods on triangle mesh generation. Each score is the mean ranking-based score over all participants (range

[0,3]

; 1st=3, 2nd=2, 3rd=1, others=0).

Figure 11. Qualitative comparison with PartUV ( Wang et al., 2025c ) . Our method generates an artist mesh (a) together with explicit UV segmentation (b). By applying angle-based UV unwrapping from Blender ( Blender, 2025 ) , we further obtain a high-quality 2D UV layout (c). In contrast, PartUV relies on a PartField ( Liu et al., 2025c ) pre-segmentation pipeline; regardless of whether it is applied to our generated mesh (d) or the ground-truth (GT) mesh (e), its resulting UV charts are consistently less clean and less well-structured than ours.

Figure 12. Gallery of UV unwrapping results using our generated UV segmentation. The shapes are taken from Fig. 8 , and both UV unwrapping and visualization are obtained through the unwrapping algorithm in Blender ( Blender, 2025 ) .

Figure 13. Comparison with MeshMosaic ( Xu et al., 2025 ) . Our method yields cleaner, more regular segmentation and mitigates the issue of overly long seams.

	PartUV w/ Our Mesh	PartUV w/ GT Mesh	Ours
Scores	2.04	1.36	2.6
Variance	0.49	0.36	0.38

Table 4. User study with PartUV ( Wang et al., 2025c ) . The scores are calculated based on the rankings and range from

[0,3]

.

Method	L2 Stretch ( $\downarrow$ to 1)	Area Dist. ( $\downarrow$ )	Angle Dist. ( $\downarrow$ to 1)	Sym. Dirichlet ( $\downarrow$ to 4)
PartField	0.921	0.849	1.256	8.283
SATO	0.979	0.562	1.128	5.156
Ground Truth	1.010	0.146	1.138	4.102

Table 5. UV distortion comparison. Our segmentation produces consistently lower UV distortion than PartField ( Liu et al., 2025c ) across all four standard metrics with Blender’s ( Blender, 2025 ) angle-based unwrapping.

Figure 14. Comparison with MeshSilksong ( Song et al., 2025 ) . Our method produces higher-quality meshes and cleaner, more coherent segmentation.

Figure 15. Texture painting with our UV unwrapping. The high-quality UV unwrapping produced by our method makes it easy for artists to paint texture maps.

Figure 16. Qualitative comparison with BPT ( Weng et al., 2025 ) and DeepMesh ( Zhao et al., 2025 ) on diverse shapes. Compared with prior triangle-mesh generation models, our method more consistently generates high-quality quadrilateral meshes, is more stable, and additionally predicts native UV segmentation.

Figure 17. Qualitative comparison with quad remeshing and reconstruction methods. Due to reliance on quadrilateral parameterization, these methods typically struggle to produce highly simplified quad meshes. In contrast, our method can generate meshes with diverse densities and additionally supports native UV segmentation.

	IM	QuadriFlow	Quadwild	NeurCross	CrossGen	Ours
Scores	0.12	0.28	1.08	1.48	1.24	1.8
Variance	0.20	0.44	1.23	1.32	1.28	1.28

Table 6. User study with remeshing and reconstruction methods on quad mesh generation. The scores are calculated based on the rankings and range from

[0,3]

.

Method	$\mathrm{NC}\uparrow$	$\mathrm{CD}\downarrow$	$\mathrm{HD}\downarrow$	$\mathrm{F1}\uparrow$
IM	0.917	0.007	0.052	0.304
QuadriFlow	0.924	0.005	0.080	0.451
QuadWild	0.970	0.001	0.020	0.848
NeurCross	0.968	0.002	0.020	0.846
CrossGen	0.969	0.001	0.020	0.849
Ours	0.971	0.001	0.020	0.857

Table 7. Geometric metrics on the quad mesh from Fig. 17 . Remeshing methods achieve near-identical fidelity to the input since they operate directly on the ground-truth geometry, whereas our method generates the mesh from a point cloud.

Methods	Token Length $\downarrow$	Transitions $\downarrow$	Time (s) $\downarrow$	Training Speed (Steps/s) $\uparrow$	Infer (Tokens/s) $\uparrow$
DeepMesh	24674	1654	2.061	0.442	$\sim$ 55
Ours	20830	981	0.319	0.488	$\sim$ 58

Table 8. Quantitative comparison with the DeepMesh ( Zhao et al., 2025 ) tokenizer. Under the overfitting setup in Fig. 18 , we report compression and training metrics for both tokenizers. Our method achieves better training speed and a higher compression ratio.

Figure 18. Ablation with the DeepMesh ( Zhao et al., 2025 ) tokenizer. We constructed an overfitting ablation to compare our tokenizer with DeepMesh. Our tokenizer converges faster and is easier for the network to learn, even when augmented with UV segmentation.

Tokenizer	$\mathrm{NC}\uparrow$	$\mathrm{CD}\downarrow$	$\mathrm{HD}\downarrow$	$\mathrm{F1}\uparrow$
DeepMesh	0.908	0.022	0.108	0.455
Ours	0.925	0.014	0.103	0.560

Table 9. Large-scale tokenizer ablation. Both models use the same architecture, data, and training budget (

64\times

A800, 200K steps); only the tokenizer differs.

Figure 19. Ablation on UV training strategies with a manually generated unseen test shape. All methods use the same test shape, which is manually created with Rodin ( Zhang et al., 2024a ) from an input text prompt cute bunny astronaut toy , converted into an SDF, and then meshed with marching cubes. This construction guarantees that the shape lies strictly outside the training set. We compare SOTA methods and three training strategies: (a) using a pretrained point cloud VAE with UV segmentation data, (b) training from scratch on UV data, and (c) first training from scratch without UV data followed by post training on UV data. The first two strategies that directly use UV data cannot reliably align with the input shape and usually collapse to an almost random shape.

Figure 20. Ablation on quad-mesh fine-tuning. After quad-mesh fine-tuning, the meshes in the black boxed region become markedly higher quality and more artist-aligned, with cleaner structure and easier downstream editing.

Figure 21. Generation from image and text prompts. By leveraging CLAY ( Zhang et al., 2024a ) for 3D generation, SATO can produce high-quality artist meshes with native UV segmentation from either an input image or a text prompt.

Figure 22. Diversity results. Conditioned on the same input, our model generates diverse meshes and segmentation outcomes, demonstrating strong generative diversity.

Figure 23. Failure cases. Left: Degenerate triangle faces arising from quadrilateral strip decoding under irregular strip configurations. Right: Suboptimal quad layouts limited by the scale and consistency of available high-quality quad-mesh training data.

Strips as Tokens: Artist Mesh Generation with Native UV Segmentation

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