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

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3d-mesh-generation factorized-flow-matching vertex-flow topology-flow latent-representation generative-model computer-graphics

Summary

LATO.2 is a factorized flow matching framework for 3D mesh generation that decomposes the process into a vertex flow followed by a connectivity flow conditioned on the vertices, achieving state-of-the-art geometric fidelity and connectivity quality.

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.
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Source: https://huggingface.co/papers/2607.10623

Abstract

Flowmatchingovercarefullydesignedlatentrepresentationshasrecentlyemergedasapowerfulparadigmfortopology-awaremeshgeneration.Existingapproaches,however,modelverticesandconnectivityjointlyinajointlatentspace,entanglingcontinuousvertexgeometrywithdiscretecombinatorialstructure;thiscomplicatesflowlearningandmanifestsasdriftingverticesandbrokensurfaces.WepresentLATO.2,afactorizedflowmatchingframeworkthatdecomposesmeshgenerationintoavertexflowfollowedbyaconnectivityflowconditionedontherealizedvertices,withbothstagesanchoredtoasharedcoarsevoxelscaffold.DedicatedVAEsunderpinthetwostages,recoveringverticesatsub-voxelprecisionandembeddingdiscreteconnectivityintoacontinuouslatentspace.Wedemonstratetwoadvantagesuniquetothisfactorization:(i)part-wisegeneration,inwhichthescaffoldispartitionedandeachpartsynthesizedatfulllatentcapacity,yieldingsubstantiallyhigher-resolutionmeshesthanamonolithiclatentpermits;and(ii)topology-adaptiveediting,inwhichmanipulatingfirst-stageverticesinducesthecorrespondingconnectivitywithoutre-optimization.ExperimentsshowthatLATO.2surpassesstate-of-the-arttopology-awaremeshgeneratorsingeometricfidelityandconnectivityquality.

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