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This paper introduces Nexus, a novel diffusion-based approach for generating high-quality triangle meshes by decoupling vertex and topology generation. By treating mesh vertices as sparse voxels in an octree structure and employing a diffusion model for coarse-to-fine vertex generation, the authors effectively mitigate the errors associated with traditional autoregressive methods. Extensive experiments reveal that Nexus significantly outperforms state-of-the-art techniques, with a user study indicating a strong preference for the generated meshes among 3D practitioners.
Nexus achieves superior mesh generation by decoupling vertex and topology creation, outperforming traditional methods that struggle with error accumulation.
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 Space-time 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.
Tsinghua AI