通过结构化推测加速3D网格生成，速度翻倍且质量更高
FlashMesh: Faster and Better Autoregressive Mesh Synthesis via Structured Speculation
- 引入预测-纠正-验证范式，利用网格结构相关性并行推测多令牌
- 在相同硬件下实现最高2倍加速，同时提升生成精度
- 适合需要快速生成高质量3D模型的交互式或大规模应用
自回归模型可通过逐个生成顶点和面来生成高质量3D网格，但其逐标记解码导致推理速度慢，限制了在交互式与大规模场景中的应用。本文提出FlashMesh，一种基于结构化推测的快速高保真网格生成框架。核心思想是网格标记具有强结构与几何相关性，可进行高置信度的多标记推测。FlashMesh针对常用的hourglass Transformer架构设计了推测解码方案，支持在面、点、坐标层级上的并行预测。大量实验表明，FlashMesh相比标准自回归模型最高可实现2倍加速，同时提升生成保真度。结果证明，网格数据中的结构先验可系统性用于加速和增强自回归生成。
原文摘要 · Abstract (English)
Autoregressive models can generate high-quality 3D meshes by sequentially producing vertices and faces, but their token-by-token decoding results in slow inference, limiting practical use in interactive and large-scale applications. We present FlashMesh, a fast and high-fidelity mesh generation framework that rethinks autoregressive decoding through a predict-correct-verify paradigm. The key insight is that mesh tokens exhibit strong structural and geometric correlations that enable confident multi-token speculation. FlashMesh leverages this by introducing a speculative decoding scheme tailored to the commonly used hourglass transformer architecture, enabling parallel prediction across face, point, and coordinate levels. Extensive experiments show that FlashMesh achieves up to a 2 x speedup over standard autoregressive models while also improving generation fidelity. Our results demonstrate that structural priors in mesh data can be systematically harnessed to accelerate and enhance autoregressive generation.
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