Topology-Preserved Auto-regressive Mesh Generation in the Manner of Weaving Silk

TL;DR

This paper introduces a topology-aware auto-regressive mesh generation method that preserves geometric properties and improves compression efficiency, addressing limitations of previous approaches that ignored mesh topology.

Contribution

We propose a novel topology-preserving mesh tokenization algorithm with vertex layering, and an online data processing strategy to enhance mesh generation quality and dataset scalability.

Findings

Achieved state-of-the-art compression ratios.

Generated meshes with improved topological integrity.

Enhanced geometric consistency in mesh synthesis.

Abstract

Existing auto-regressive mesh generation approaches suffer from ineffective topology preservation, which is crucial for practical applications. This limitation stems from previous mesh tokenization methods treating meshes as simple collections of equivalent triangles, lacking awareness of the overall topological structure during generation. To address this issue, we propose a novel mesh tokenization algorithm that provides a canonical topological framework through vertex layering and ordering, ensuring critical geometric properties including manifoldness, watertightness, face normal consistency, and part awareness in the generated meshes. Measured by Compression Ratio and Bits-per-face, we also achieved state-of-the-art compression efficiency. Furthermore, we introduce an online non-manifold data processing algorithm and a training resampling strategy to expand the scale of trainable dataset and avoid costly manual data curation. Experimental results demonstrate the effectiveness of our approach, showcasing not only intricate mesh generation but also significantly improved geometric integrity.