TrieMesh: Structured Frontier Representation for Autoregressive Artistic Mesh Generation

Autoregressive artistic mesh generation has progressed via compact tokenization, yet each prediction step still mainly relies on sequence history to represent the current mesh state. The active frontier is an important part of this state, but is rarely represented explicitly in a structured and queryable form. We present TrieMesh, a frontier-guided autoregressive representation that maintains active frontier edges in a Trie with canonical lexicographic keys, enabling deterministic selection of the next edge for expansion. A shared autoregressive decoder switches between full-face generation when the frontier is empty and completion-vertex prediction otherwise, reusing the two known endpoints of the active edge to avoid redundant coordinate prediction. Experiments show improved geometric fidelity over recent autoregressive baselines under comparable token budgets. Beyond global generation, the same structured frontier representation naturally supports mesh hole filling, where the boundary edges of a missing region initialize the active frontier. This allows global mesh generation and local mesh editing to follow the same frontier-guided autoregressive expansion process.