# MeshWeaver：稀疏体素引导的自回归网格生成框架

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-06-03 08:00
- AIHOT 分数：63
- AIHOT 链接：https://aihot.virxact.com/items/cmpyw41oa03vosli3efoht7yj
- 原文链接：https://arxiv.org/abs/2606.04688

## AI 摘要

MeshWeaver提出一种自回归网格生成框架，将网格生成视为表面编织过程，直接预测下一个顶点而非独立坐标。其核心是多级稀疏体素编码器，通过三种方式注入几何上下文：体素特征作为顶点表示、交叉注意力引导token预测、以及作为结构骨架约束生成。层次化设计可在单解码步骤中实现从粗到细的顶点预测。实验表明，MeshWeaver达到18%的压缩比（SOTA），可生成最多16K面网格，并在几何保真度上显著超越此前方法。

## 正文

Autoregressive mesh generation has gained attention by tokenizing meshes into sequences and training models in a language-modeling fashion. However, existing approaches suffer from two fundamental limitations: (i) low tokenization efficiency, which yields long token sequences and prevents scaling to high-poly meshes, and (ii) absence of geometry-aware guidance, as generation is conditioned only on global shape embeddings rather than local surface cues. We introduce MeshWeaver, an autoregressive framework that treats mesh generation as a surface weaving process by directly predicting the next vertex instead of independent coordinates. At its core is a multi-level sparse-voxel encoder that injects geometric context into the generative process in three complementary ways: providing voxel features as vertex representations, guiding token prediction via cross-attention to voxel features, and serving as a structural scaffold that constrains generation around the input surface. Our hierarchical design enables coarse-to-fine vertex prediction in a single decoding step, while tightly coupling the generative model with 3D geometry. Extensive experiments demonstrate that MeshWeaver achieves a state-of-the-art compression ratio of 18%, can generate meshes with up to 16K faces, and significantly improves geometric fidelity over prior approaches.