用强化学习优化3D模型接缝生成，减少纹理扭曲和碎片化。
SeamCrafter: Enhancing Mesh Seam Generation for Artist UV Unwrapping via Reinforcement Learning
- 基于点云的自回归接缝生成器，分离拓扑与几何特征。
- 通过偏好优化降低90%以上纹理畸变，接缝更少且连续。
- 适合需要高质量纹理映射的美术师和三维建模工程师。
网格接缝在3D表面分割中对UV参数化和贴图至关重要。不当的接缝位置常导致严重UV畸变或过度碎片化，影响纹理合成并打断艺术家工作流。现有方法往往在高畸变与多分散块之间权衡。为此，我们提出SeamCrafter，一种基于点云输入的自回归GPT式接缝生成器。该模型采用双分支点云编码器，在预训练阶段解耦并捕捉互补的拓扑与几何线索。为进一步提升接缝质量，我们在一个新型接缝评估框架构建的偏好数据集上，使用直接偏好优化（DPO）进行微调。该框架主要依据UV畸变和碎片化评估接缝，并提供成对偏好标签以指导优化。大量实验表明，SeamCrafter生成的接缝在畸变和碎片化方面显著优于先前方法，同时保持拓扑一致性与视觉保真度。
原文摘要 · Abstract (English)
Mesh seams play a pivotal role in partitioning 3D surfaces for UV parametrization and texture mapping. Poorly placed seams often result in severe UV distortion or excessive fragmentation, thereby hindering texture synthesis and disrupting artist workflows. Existing methods frequently trade one failure mode for another-producing either high distortion or many scattered islands. To address this, we introduce SeamCrafter, an autoregressive GPT-style seam generator conditioned on point cloud inputs. SeamCrafter employs a dual-branch point-cloud encoder that disentangles and captures complementary topological and geometric cues during pretraining. To further enhance seam quality, we fine-tune the model using Direct Preference Optimization (DPO) on a preference dataset derived from a novel seam-evaluation framework. This framework assesses seams primarily by UV distortion and fragmentation, and provides pairwise preference labels to guide optimization. Extensive experiments demonstrate that SeamCrafter produces seams with substantially lower distortion and fragmentation than prior approaches, while preserving topological consistency and visual fidelity.
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