{"ID":6536154,"CreatedAt":"2026-07-14T01:21:01.169441415Z","UpdatedAt":"2026-07-15T03:28:55.185153975Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.10623","arxiv_id":"2607.10623","title":"LATO.2: Factorized 3D Mesh Generation with Vertex and Topology Flow","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.","short_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 co...","url_abs":"https://arxiv.org/abs/2607.10623","url_pdf":"https://arxiv.org/pdf/2607.10623v1","authors":"[\"Hang Long\",\"Tianhao Zhao\",\"Junkai Lin\",\"Youjia Zhang\",\"Huipeng Guo\",\"Rendong Liang\",\"Jiale Xu\",\"Jozef Hladký\",\"Matthias Nießner\",\"Wei Yang\"]","published":"2026-07-12T07:38:06Z","proceeding":"cs.GR","tasks":"[\"cs.GR\",\"cs.CV\"]","methods":"[\"Variational Autoencoder\"]","has_code":false}