QuadGPT: Native Quadrilateral Mesh Generation with Autoregressive Models
Hong Kong University of Science and Technology · Zhejiang University · Department of Computer Science and Engineering, Hong Kong University of Science and Technology · South China University of Technology · Institute of automation, Chinese academy of science, Chinese Academy of Sciences · Westlake University · Beijing University of Posts and Telecommunications · Tencent hunyuan · Tencent · Huawei Technologies Ltd.
PDF 由论文原始站点提供，PaperCompass 不保存论文文件。
摘要
The generation of quadrilateral-dominant meshes is a cornerstone of professional 3D content creation. However, existing generative models generate quad meshes by first generating triangle meshes and then merging triangles into quadrilaterals with some specific rules, which typically produces quad meshes with poor topology. In this paper, we introduce QuadGPT, the first autoregressive framework for generating quadrilateral meshes in an end-to-end manner. QuadGPT formulates this as a sequence prediction paradigm, distinguished by two key innovations: a unified tokenization method to handle mixed topologies of triangles and quadrilaterals, and a specialized Reinforcement Learning fine-tuning method tDPO for better generation quality. Extensive experiments demonstrate that QuadGPT significantly surpasses previous triangle-to-quad conversion pipelines in both geometric accuracy and topological quality. Our work establishes a new benchmark for native quad-mesh generation and showcases the power of combining large-scale autoregressive models with topology-aware RL refinement for creating structured 3D assets.