直接生成三角网格，18倍提速且保持高质量。
MeshFlow: Mesh Generation with Equivariant Flow Matching
- 基于对称性不变的流匹配，直接生成三角网格。
- 生成质量媲美顶尖自回归方法，推理快18倍。
- 适合需要高效3D生成的科研与工业应用。
三角网格是常见的3D场景表示形式，但直接生成网格极具挑战，因其包含重要对称性：面和顶点的排列不变性。MeshFlow 直接将网格作为三角形点云生成，无需将其序列化为长自回归序列。我们采用具备等变性质的最优传输流匹配模型，尊重三角形点云的关键对称性：任意面的排列以及每个面内顶点的排列。为此，我们对Diffusion Transformer架构进行了简单但有效的修改，构建了一个可扩展网络，能在保持所需等变性的同时建模速度场。此外，我们引入基于最优传输的训练目标，通过消除违反对称性的监督信号，提升收敛性。MeshFlow 在生成质量上达到当前自回归网格生成器的水平，同时推理速度提升约18倍。项目主页见 https://qiisun.github.io/MeshFlow/。
原文摘要 · Abstract (English)
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 18$\times$ speedup during inference. Project page is at https://qiisun.github.io/MeshFlow/.
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