  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.
