TriFlow: Generating Artist-Like 3D Mesh Topology via Nearest-Vertex Vector Fields
For 3D content creation, TriFlow provides a way to automatically produce high-quality, artist-like mesh topologies, significantly outperforming existing learning-based approaches.
TriFlow generates compact 3D meshes with artist-like triangle topology from input geometry, achieving 90% lower Chamfer Distance and 8x speedup over prior methods.
We present TriFlow, a new generative approach for producing compact 3D meshes with artist-like triangle topology directly from input geometry conditions such as signed distance fields. Our key insight is to represent mesh topology as a nearest-vertex vector field (NVF) defined over the surface, where each point encodes its association to the nearest triangle vertex in the local barycentric frame. We train a latent flow-matching model to synthesize this field, enabling topology generation conditioned on the input geometry. To extract a coherent mesh, we cluster surface regions using the generated NVF and guide a constrained quadric error metric (QEM) mesh simplification with topology-aware optimization. This yields output meshes that closely match the input geometry while exhibiting structured, artist-like connectivity. Experiments demonstrate that TriFlow achieves stronger generalization and significantly improved topology quality compared to state-of-the-art learning-based approaches, alongside 90% lower Chamfer Distance and an 8x speedup.