LATO.2: Factorized 3D Mesh Generation with Vertex and Topology Flow

2026-07-12

AI Summary

Existing topology-aware mesh generation methods couple geometry and topology within a shared latent space, often leading to vertex drift and surface discontinuities. This work proposes a decomposed flow-matching framework that first generates high-fidelity vertices based on a shared coarse voxel scaffold and subsequently produces connectivity conditioned on these vertices. The approach employs two dedicated variational autoencoders: one for sub-voxel-level vertex reconstruction and another for continuous latent embedding of discrete connectivity patterns. This design enables part-wise generation and automatic topological adaptation. Experimental results demonstrate that the proposed method outperforms state-of-the-art approaches in both geometric fidelity and connection quality.

Abstract

Flow matching over carefully designed latent representations has recently emerged as a powerful paradigm for topology-aware mesh generation. Existing approaches, however, model vertices and connectivity jointly in a joint latent space, entangling continuous vertex geometry with discrete combinatorial structure; this complicates flow learning and manifests as drifting vertices and broken surfaces. We present LATO.2, a factorized flow matching framework that decomposes mesh generation into a vertex flow followed by a connectivity flow conditioned on the realized vertices, with both stages anchored to a shared coarse voxel scaffold. Dedicated VAEs underpin the two stages, recovering vertices at sub-voxel precision and embedding discrete connectivity into a continuous latent space. We demonstrate two advantages unique to this factorization: (i) part-wise generation, in which the scaffold is partitioned and each part synthesized at full latent capacity, yielding substantially higher-resolution meshes than a monolithic latent permits; and (ii) topology-adaptive editing, in which manipulating first-stage vertices induces the corresponding connectivity without re-optimization. Experiments show that LATO.2 surpasses state-of-the-art topology-aware mesh generators in geometric fidelity and connectivity quality.

Problem

• 3D mesh generation
• topology-aware
• vertex connectivity
• latent representation
• flow matching

Innovation

• factorized flow matching
• vertex and topology flow
• part-wise generation
• topology-adaptive editing
• latent mesh generation