SeamGen：通过图流匹配生成与艺术家对齐的UV接缝

中文摘要 AI 辅助

UV接缝放置是3D内容创作中关键但劳动密集型的步骤，现有自动方法存在局限。我们提出SeamGen，一种与艺术家偏好和生产要求对齐的UV接缝生成模型。它通过流匹配生成模型从大量现有接缝布局中学习每条边接缝标签的分布。设计了Mesh Transformer主干，利用流模型的免训练修复能力。实验表明SeamGen生成的UV布局与艺术家创作偏好更契合，感知质量更优。

英文摘要

UV seam placement is a critical yet labor-intensive step in 3D content creation, requiring artists to balance chart shape, seam concealment, and alignment with semantic and geometric features. Existing automatic methods are primarily based on per-object optimization, relying on handcrafted objectives to avoid distortion or on proxies from pretrained models to inject semantic information. However, these strategies are not always well aligned with seams used in industrial production pipelines, often resulting in layouts that deviate from artist-preferred seam patterns and practical production requirements. To address these limitations, we propose SeamGen, a generative model for UV seam generation that aligns with artist preferences and production requirements. Instead of depending on manually designed objectives and constraints, SeamGen learns the distribution of per-edge seam labels from a large corpus of existing seam layouts using a flow-matching generative model. A key challenge is that typical Transformer architectures used in flow matching models are designed for sequential representations, such as point clouds, and cannot naturally account for mesh topology. To enable mesh-native learning, we design a Mesh Transformer backbone that interleaves local graph attention over mesh edges with global self-attention across vertices, capturing both fine-grained geometric cues and long-range topological coherence. To further improve inference-time controllability and quality, we exploit the training-free inpainting capability of flow models for both localized seam refinement and constraint-guided seam generation. Extensive experiments show that by learning priors from professional seam layout data, SeamGen produces UV layouts that better align with artist-authored preferences and achieve superior perceptual quality compared with distortion-based and semantic-proxy baselines.

发表机构

• State Key Lab of CAD&CG, Zhejiang University（浙江大学计算机辅助设计与图形学国家重点实验室）
• VAST（未提及，可能是一个缩写，无法准确翻译）

机构由 AI 辅助整理，请以论文原文为准。