Toggle each card to compare the input geometry against the TriFlow output (shaded + wireframe). Drag to orbit, scroll to zoom.
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 **8× speedup**.
We introduce a novel generative approach to create compact, artist-like mesh topologies from signed distance field (SDF) inputs.
Our method consists of three major components:
TriFlow can target different topology budgets while keeping the input geometry. Switch between Input and three levels of detail to see how connectivity adapts.
Several concurrent efforts also tackle generative mesh topology by modeling a field:
@inproceedings{li2026triflow,
title = {TriFlow: Generating Artist-Like 3D Mesh Topology via Nearest-Vertex Vector Fields},
author = {Li, Haoxuan and Erko{\c{c}}, Ziya and Sirigatti, Daniele and Rosov, Vladislav and Li, Lei and Dai, Angela and Nie{\ss}ner, Matthias},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2026},
}