SeamFlow: Structure-Aware Flow Matching on Edge Probabilities for Artist-Like UV Unwrapping

2026-09-04

AI Summary

为解决3D表面切割和UV展开中的视觉语义一致性问题，SeamFlow通过在高维边概率空间中进行连续流匹配，并结合局部拓扑令牌与全局形状先验，提高了拓扑感知能力并减少了参数化失真。

Abstract

3D surface cutting and UV unwrapping are fundamental problems in computer graphics. Traditional geometric optimization methods mainly focus on reducing parameterization distortion, but they often overlook visual semantic coherence in seam layouts. Recent autoregressive generative methods improve semantic coherence, yet limited perception of mesh topology often causes inaccurate local cuts. To address these limitations, we introduce SeamFlow, a novel generative framework for 3D surface cutting. We reformulate the discrete mesh-cutting problem as continuous flow matching in a high-dimensional edge-probability space. Through continuous relaxation, SeamFlow learns a deterministic mapping from a Gaussian prior to a target seam-probability distribution. An evolution network couples local topological tokens with global shape priors and guides smooth probability flow through Ordinary Differential Equation solving. Compared with existing autoregressive generative frameworks, SeamFlow improves topology awareness through edge tokenization while eliminating both 3D spatial projection errors and artificial sequential-order bias. Extensive experiments demonstrate that SeamFlow achieves exceptional semantic coherence and remarkably low parameterization distortion. The project page is https://meshy-dev.github.io/seamflow.

Problem

• 3D surface cutting
• UV unwrapping
• semantic coherence
• topology awareness
• parameterization distortion

Innovation

• continuous flow matching
• edge tokenization
• topology awareness
• evolution network
• semantic coherence