LATO.2：基于顶点与拓扑流的分解式三维网格生成

摘要

在精心设计的潜在表示上进行流匹配，近期已成为一种用于拓扑感知网格生成的强大范式。然而，现有方法将顶点与连通性共同建模于一个联合潜在空间中，使得连续的顶点几何形状与离散的组合结构相互纠缠，这增加了流学习的复杂性，并表现为顶点漂移和表面断裂。我们提出LATO.2，一种分解式流匹配框架，它将网格生成分解为顶点流以及基于已生成顶点条件化的连通性流，且两个阶段均锚定于共享的粗体素支架。专用的VAE支撑这两个阶段，以亚体素精度恢复顶点，并将离散的连通性嵌入到连续潜在空间中。我们展示了这种分解独有的两大优势：（i）分部件生成——将支架划分为多个部分，每个部分以完整的潜在容量进行合成，从而生成比单一潜在表示所能实现的显著更高分辨率的网格；（ii）拓扑自适应编辑——通过操控第一阶段的顶点，无需重新优化即可诱导相应的连通性。实验表明，LATO.2在几何保真度和连通性质量上超越了当前最先进的拓扑感知网格生成方法。

English

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.