The polygon mesh representation of 3D data exhibits great flexibility, fast rendering speed, and storage efficiency, which is widely preferred in various applications. However, given its unstructured graph representation, the direct generation of high-fidelity 3D meshes is challenging. Fortunately, with a pre-defined ordering strategy, 3D meshes can be represented as sequences, and the generation process can be seamlessly treated as an auto-regressive problem. In this paper, we validate the Neural Coordinate Field (NeurCF), an explicit coordinate representation with implicit neural embeddings, is a simple-yet-effective representation for large-scale sequential mesh modeling. After that, we present MeshXL, a family of generative pre-trained auto-regressive models, which addresses the process of 3D mesh generation with modern large language model approaches. Extensive experiments show that MeshXL is able to generate high-quality 3D meshes, and can also serve as foundation models for various down-stream applications.

验证了NeurCF作为大规模顺序网格建模的简单有效表示，提出了MeshXL，一种基于现代大型语言模型方法处理3D网格生成的生成式预训练自回归模型家族。通过广泛实验表明MeshXL能生成高质量的3D网格，并可作为各种下游应用的基础模型。

MeshXL：用于生成式三维基础模型的神经坐标场