Title: MeshMosaic: Scaling Artist Mesh Generation via Local-to-Global Assembly
Primary area: generative models
Keywords: Artist Mesh Generation, Autoregressive

Abstract: 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.

TL;DR: We propose MeshMosaic, a novel local-to-global framework for artist mesh generation that scales to over 100K triangles.