# PolyFlow：面向艺术家风格网格生成的连续拓扑嵌入流匹配框架

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-06-25 08:00
- AIHOT 分数：49
- AIHOT 链接：https://aihot.virxact.com/items/cmr1imjwa036oslnl3vc7n8xi
- 原文链接：https://arxiv.org/abs/2606.30673

## AI 摘要

自回归Transformer可生成高质量网格拓扑，但串行解码计算量比并行模型慢数个数量级；连续扩散与流匹配方法无法直接处理离散网格。PolyFlow提出紧凑拓扑嵌入器，将离散顶点位置和法线投影为连续逐顶点嵌入，通过时空距离阈值忠实恢复原始邻接信息。预训练并冻结该嵌入器后，任意网格可转换为统一连续顶点状态空间。基于此表示，PolyFlow采用Transformer流匹配框架，对提取的点云特征条件化，实现完全并行顶点状态去噪；推理时通过ODE求解器快速生成，并支持直接指定目标顶点数精确控制分辨率。在Toys4K基准上，PolyFlow的Chamfer距离和Hausdorff距离均超越现有自回归基线。

## 正文

Autoregressive Transformers dominate high-quality mesh generation by producing artist-worthy topologies, yet their inherent sequential decoding induces substantial computational overhead, falling orders of magnitude slower than parallel generative models. On the other hand, while continuous diffusion and flow-matching methods support efficient parallel synthesis across a variety of domains, they cannot be directly applied to meshes: mesh connectivity is inherently discrete and incompatible with standard continuous noise injection and denoising operations. To resolve this fundamental incompatibility, we introduce a compact topology embedder that projects discrete mesh vertex positions and normals into continuous per-vertex embeddings, where the original discrete adjacency information can be faithfully recovered via spacetime distance thresholding. After pretraining and freezing this embedder, any raw mesh can be fully converted into a continuous per-vertex state space unifying position, normal, and implicit topological attributes. Built upon this novel continuous mesh representation, we present PolyFlow, a Transformer-based flow-matching framework that achieves fully parallel vertex state denoising conditioned on extracted point-cloud features. During inference, our model completes generation rapidly via an ODE solver, and supports explicit, precise control over output mesh resolution by directly specifying the target vertex count. Extensive evaluations on the Toys4K benchmark demonstrate that PolyFlow surpasses state-of-the-art autoregressive baselines in both Chamfer Distance and Hausdorff Distance.