MeshOctave generates meshes via cascading resolution transitions

2026-09-30

AI Summary

This study addresses the slow generation of autoregressive models and the reliance of continuous flow models on heuristic decoders by proposing MeshOctave. This framework introduces a novel octree-based binary spatial mesh scale definition that treats coarsening as deterministic vertex merging, thereby supporting unordered set modeling and dynamic adaptive refinement. Furthermore, it employs a scale-conditioned masked uniform discrete diffusion model to learn split-reconnection operations, enabling parallel, serialization-free hierarchical mesh generation. Experimental results demonstrate that MeshOctave significantly outperforms baseline methods in both geometric fidelity and topological validity, while naturally extending to mesh subdivision tasks.

Abstract

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 intra-scale token prediction and coarse-to-fine refinement from global structure to local topology; yet, existing methods derive hierarchical scales via progressive mesh simplification and invert them sequentially. This eliminates intra-scale parallelism and scales generation steps linearly with face count. In this paper, 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 structural tokens. 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 operation from resolution collapse hierarchies. MeshOctave outperforms strong baselines in geometric fidelity and topological validity by a non-trivial margin, while supporting adaptive resolution refinement and extending naturally to mesh subdivision tasks.

Problem

• 3D mesh generation
• autoregressive models
• multi-scale generation
• topology
• parallel decoding

Innovation

• Mesh Generation
• Discrete Diffusion
• Octree Resolution
• Split-and-Rewire
• Adaptive Refinement