TriFlow: Generating Artist-Like 3D Mesh Topology via Nearest-Vertex Vector Fields

We present TriFlow, a new generative approach for produc-ing compact 3D meshes with artist-like triangle topology directly frominput geometry conditions such as signed distance fields. Our key insightis to represent mesh topology as a nearest-vertex vector field (NVF) de-fined over the surface, where each point encodes its association to thenearest triangle vertex in the local barycentric frame. We train a latentflow-matching model to synthesize this field, enabling topology genera-tion conditioned on the input geometry. To extract a coherent mesh, wecluster surface regions using the generated NVF and guide a constrainedquadric error metric mesh simplification with topology-aware optimiza-tion. This yields output meshes that closely match the input geometrywhile exhibiting structured, artist-like connectivity. Experiments demon-strate that TriFlow achieves stronger generalization and significantlyimproved topology quality compared to state-of-the-art learning-basedapproaches, alongside 90% lower Chamfer Distance and an 8× speedup.