以條帶為單位：具原生UV分割的藝術家網格生成

摘要

近期自迴歸變壓器的進展展現出生成藝術家級網格的卓越潛力。然而，現有方法採用的標記排序策略通常難以達到專業藝術家標準——基於座標的排序會產生效率低下的冗長序列，而基於面片的啟發式方法則會破壞高品質建模所需的連續邊緣流動性與結構規律性。為解決這些局限，我們提出「條帶即標記」（SATO）框架，其採用受三角形條帶啟發的新型標記排序策略。通過將序列構建為明確編碼UV邊界的連接面鏈，我們的方法能自然保留藝術家創作網格特有的組織化邊緣流動性與語義佈局。此公式的關鍵優勢在於其統一表示法，使得同一標記序列可解碼為三角形或四邊形網格。這種靈活性支持對兩類資料的聯合訓練：大規模三角形資料提供基礎結構先驗，而高品質四邊形資料則增強輸出的幾何規律性。大量實驗表明，SATO在幾何品質、結構連貫性與UV分割方面均持續超越現有方法。

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.

以條帶為單位：具原生UV分割的藝術家網格生成

Strips as Tokens: Artist Mesh Generation with Native UV Segmentation

摘要

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