MeshOctave: Vertex Split-and-Rewire Cascades for Native Mesh Generation

Generating compact, artist-style meshes with explicit topology typically relies on autoregressive models—which incur prohibitive sequential per-token costs—or continuous flow models that depend on heuristic connectivity decoders. Next-scale generation paradigms offer a compelling alternative by enabling parallel token prediction within each scale; yet, existing methods derive hierarchical levels via progressive mesh simplification and invert them sequentially. This eliminates intra-scale parallelism and scales generation steps linearly with face count. We propose MeshOctave, which instead defines scale through dyadic spatial grid resolutions, framing coarsening as a deterministic collapse that merges vertices sharing a voxel cell and inherits connectivity. Its inverse operation, split-and-rewire, determines which octant sub-vertices are instantiated for each coarse face and resolves local connectivity using discrete refinement tokens. Because these per-face operations require no serialization, each scale transition is modeled as an unordered set that adds one bit of coordinate precision, naturally supporting dynamic-length meshes and adaptive resolution refinement. We construct a scale-conditioned masked-uniform discrete diffusion model to learn split-and-rewire refinement from resolution collapse hierarchies. MeshOctave outperforms strong baselines in geometric fidelity and topological validity, while supporting adaptive resolution refinement and extending naturally to mesh subdivision tasks.