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arXiv · 2511.15618

FlashMesh: Faster and Better Autoregressive Mesh Synthesis via Structured Speculation

Tingrui Shen·Yiheng Zhang·Chen Tang·Chuan Ping·Zixing Zhao·Le Wan·Yuwang Wang·Ronggang Wang·Shengfeng He
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Abstract

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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Tingrui Shen, Yiheng Zhang, Chen Tang, Chuan Ping, Zixing Zhao, Le Wan, Yuwang Wang, Ronggang Wang, Shengfeng He. 2025-11-19. FlashMesh: Faster and Better Autoregressive Mesh Synthesis via Structured Speculation. https://arxiv.org/abs/2511.15618

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