发表机构
MAIS, Institute of Automation, Chinese Academy of Sciences; School of Artificial Intelligence, University of Chinese Academy of Sciences; VAST; Tsinghua University; Xi’an Jiaotong University（中国科学院自动化研究所复杂系统管理与控制国家重点实验室; 中国科学院大学人工智能学院; 无（暂未找到合适中文名，VAST可音译为“瓦斯特”，但这并非一个正式的中文机构名，所以保留英文）; 清华大学; 西安交通大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对高质量三角形网格生成问题，提出Nexus扩散方法，通过解耦顶点与拓扑生成实现整体网格生成，经实验验证其性能优于现有基线，有效克服顺序网格建模局限且获从业者青睐。
AI 中文摘要
生成高质量三角形网格对电影、游戏和交互式3D应用至关重要。主流方法依赖网格序列化和自回归过程，在有效推理方面存在困难且对误差积累敏感。本文提出Nexus，一种通过解耦顶点和拓扑生成实现整体网格生成的扩散方法。先将网格顶点视为八叉树组织的稀疏体素，用扩散模型从粗到细生成顶点；为拓扑建模提出时空间隔，将任意边和面拓扑编码为连续顶点嵌入，再用扩散模型在生成顶点上生成连续嵌入。在Objaverse和Toys4K数据集及自然图像上的大量实验表明，该方法优于现有自回归和两阶段基线，有效规避顺序网格建模固有局限，3D从业者的盲测显示对其结果有强烈感知偏好。
英文摘要
Generating high-quality triangle meshes is essential for film, gaming, and interactive 3D applications. Mainstream methods rely on mesh serialization and autoregressive processes, which stuggles in effective inference and is sensitive to error accumulation. In this paper, we present Nexus, a diffusion method that achieves holistic mesh generation via decoupled vertex and topology generation. First, we view mesh vertices as sparse voxels organized as an octree and adopt a diffusion model to generate the vertices in a coarse-to-fine manner. Second, for topology modeling, we propose Spacetime Interval, as an extension of Spacetime Distance to encode arbitrary edge and face topology into continuous per-vertex embeddings. It allows for a global and efficient recovery of complex topology. We then employ a diffusion model to generate the continuous embeddings on the generated vertices. Extensive experiments on the Objaverse and Toys4K datasets and in-the-wild images demonstrate that our method outperforms state-of-the-art autoregressive and two-stage baselines, effectively circumventing the inherent limitations of sequential mesh modeling. A blind user study from 3D practitioners confirms strong perceptual preference for our results.
DOI：10.1145/3811344
