Order-Agnostic Artistic Mesh Generation via Local Expansion

We introduce MeshExpand, an order-agnostic and local framework for generating artistic meshes from input 3D shapes at high resolution. Recent learning-based methods formulate mesh generation as global sequence prediction, which couples generation to a predefined serialization order and can require long contexts as mesh resolution grows. Instead, we formulate mesh generation as learned local surface expansion. Starting from one or more seed triangles, MeshExpand iteratively selects an open boundary edge and predicts the opposite vertex from a bounded neighborhood of nearby faces and point samples, together with a global shape prior. This formulation maintains a constant-size local context per generation step that is independent of the output mesh resolution. Representing previously generated faces as an unordered set further removes dependence on a fixed generation order and enables the same model to support arbitrary growth directions, partial mesh completion, and local editing. MeshExpand also provides global and local resolution control by coupling triangle size with input point density, and uses online geometric validation and resampling to suppress invalid mesh configura- tions during generation. Experiments show that MeshExpand scales to meshes with up to 120K faces using less than 2 GB of GPU memory, transfers to out-of-distribution scanned and generated shapes with fewer degradation and improves geometric fidelity, orientation consistency, and manifoldness. Our code and model weights will be made publicly available upon paper acceptance.