通过局部到全局组装，实现超10万三角面的艺术家级网格生成
MeshMosaic: Scaling Artist Mesh Generation via Local-to-Global Assembly
- 分块生成+共享边界条件，提升高分辨率网格可扩展性
- 生成超过10万三角面，远超同类方法的约8千面上限
- 适合需要精细几何细节的工业级3D建模场景
将艺术家设计的网格扩展至高三角面数量，对自回归生成模型仍具挑战。现有基于Transformer的方法受限于长序列瓶颈和量化分辨率不足，主要因所需标记数庞大且量化粒度受限，难以忠实还原细小几何特征与结构化密度模式。我们提出MeshMosaic，一种新颖的局部到全局框架，用于艺术家网格生成，可扩展至超过10万三角面——显著超越以往方法通常仅处理约8千面的限制。MeshMosaic首先将形状分割为块，逐块自回归生成，并利用共享边界条件以促进相邻区域间的连贯性、对称性和无缝连接。该策略通过独立量化各块，实现更高对称性与组织性的网格密度与结构。在多个公开数据集上的大量实验表明，MeshMosaic在几何保真度与用户偏好上均显著优于现有最优方法，支持更优细节表达，适用于真实世界应用的高效网格生成。
原文摘要 · Abstract (English)
Scaling artist-designed meshes to high triangle numbers remains challenging for autoregressive generative models. Existing transformer-based methods suffer from long-sequence bottlenecks and limited quantization resolution, primarily due to the large number of tokens required and constrained quantization granularity. These issues prevent faithful reproduction of fine geometric details and structured density patterns. We introduce MeshMosaic, a novel local-to-global framework for artist mesh generation that scales to over 100K triangles--substantially surpassing prior methods, which typically handle only around 8K faces. MeshMosaic first segments shapes into patches, generating each patch autoregressively and leveraging shared boundary conditions to promote coherence, symmetry, and seamless connectivity between neighboring regions. This strategy enhances scalability to high-resolution meshes by quantizing patches individually, resulting in more symmetrical and organized mesh density and structure. Extensive experiments across multiple public datasets demonstrate that MeshMosaic significantly outperforms state-of-the-art methods in both geometric fidelity and user preference, supporting superior detail representation and practical mesh generation for real-world applications.
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