Papers
arxiv:2606.23489

MeshFlow: Mesh Generation with Equivariant Flow Matching

Published on Jun 22
· Submitted by
Qi Sun
on Jun 23
Authors:
,
,
,
,
,
,
,
,

Abstract

MeshFlow generates triangle meshes directly using equivariant optimal-transport flow matching models with improved inference speed over autoregressive methods.

Meshes are among the most common 3D scene representations, but directly generating meshes is challenging because the representation contains important symmetries, including permutation invariance of faces and vertices. MeshFlow learns to generate triangle meshes directly as triangle soups, avoiding the need to serialize meshes into long autoregressive sequences. We adopt equivariant optimal-transport flow matching models that respect the key symmetries of triangle soups: arbitrary permutations of faces and permutations of the vertices within each face. Toward this goal, we propose a simple yet effective modification to the Diffusion Transformer architecture, resulting in a scalable network capable of modeling a velocity field while maintaining the desired equivariance. We further introduce an optimal-transport-based training objective that improves convergence by eliminating supervision signals that violate these symmetries. MeshFlow achieves mesh quality comparable to state-of-the-art autoregressive mesh generators while providing about an 18times speedup during inference. Project page is at https://qiisun.github.io/MeshFlow/.

Community

Excited to share MeshFlow — a new approach that can generate meshes with a fraction of seconds, while achieving state-of-the-art generation quality.
Secret sources? Instead of autoregressive models, use equivariant flow-matching!
Code and pretrained checkpoints are ready! We’ll present MeshFlow next month at SIGGRAPH 2026 in LA.

Sign up or log in to comment

Get this paper in your agent:

hf papers read 2606.23489
Don't have the latest CLI?
curl -LsSf https://hf.co/cli/install.sh | bash

Models citing this paper 0

No model linking this paper

Cite arxiv.org/abs/2606.23489 in a model README.md to link it from this page.

Datasets citing this paper 0

No dataset linking this paper

Cite arxiv.org/abs/2606.23489 in a dataset README.md to link it from this page.

Spaces citing this paper 0

No Space linking this paper

Cite arxiv.org/abs/2606.23489 in a Space README.md to link it from this page.

Collections including this paper 1