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.

翻译：三维数据的多边形网格表示具有极大的灵活性、快速的渲染速度和存储效率，因此在各类应用中被广泛青睐。然而，鉴于其非结构化的图表示形式，直接生成高保真度的三维网格具有挑战性。幸运的是，通过预定义的排序策略，三维网格可以表示为序列，其生成过程可以无缝地视为自回归问题。在本文中，我们验证了神经坐标场（NeurCF）——一种具有隐式神经嵌入的显式坐标表示——是一种简单而有效的大规模序列化网格建模表示方法。在此基础上，我们提出了MeshXL，一个生成式预训练自回归模型家族，它利用现代大语言模型方法来解决三维网格生成过程。大量实验表明，MeshXL能够生成高质量的三维网格，并且可以作为各种下游应用的基础模型。