Quality-Preserving Auto-Regressive Mesh Generation Acceleration via Multi-Head Speculative Decoding

Dian Chen, Yansong Qu, Xinyang Li, Ming Li, Shengchuan Zhang

Keywords: Mesh Generation, Acceleration, Auto-Regressive Models

TL;DR: Lossless Acceleration of Auto-Regressive Mesh Generation via Multi-Head Speculative Decoding

Abstract: Current auto-regressive models can generate high-quality, topologically precise meshes; however, they necessitate thousands—or even tens of thousands—of next-token predictions during inference, resulting in substantial latency. We introduce XSpecMesh, a quality-preserving acceleration method for auto-regressive mesh generation models. XSpecMesh employs a lightweight, multi-head speculative decoding scheme to predict multiple tokens in parallel within a single forward pass, thereby accelerating inference. We further propose a verification and resampling strategy: the backbone model verifies each predicted token and resamples any tokens that do not meet the quality criteria. In addition, we propose a distillation strategy that trains the lightweight decoding heads by distilling from the backbone model, encouraging their prediction distributions to align and improving the success rate of speculative predictions. Extensive experiments demonstrate that our method achieves a speedup without sacrificing generation quality. Our code will be released.

Primary Area: generative models

Submission Number: 2295