Nexus: Native Mesh Generation with Diffusion

2026-07-15

AI Summary

This work addresses the limitations of traditional mesh generation methods, which rely on sequential or autoregressive strategies and suffer from low inference efficiency and error accumulation. The authors propose an end-to-end diffusion model framework that decouples vertex and topology generation to produce high-quality, globally consistent triangular meshes. Vertices are innovatively represented as sparse voxels organized in an octree structure, and a Spacetime Interval encoding is introduced to map arbitrary edge-face topologies into continuous vertex embeddings, enabling efficient global topology recovery. Employing a coarse-to-fine strategy for vertex generation and a separate diffusion model for topology prediction, the method significantly outperforms existing autoregressive and two-stage approaches on the Objaverse and Toys4K datasets as well as on real-world images, with user studies confirming its superior perceptual quality.

Abstract

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.

Problem

• mesh generation
• autoregressive modeling
• error accumulation
• inference efficiency
• 3D reconstruction

Innovation

• diffusion model
• mesh generation
• Spacetime Interval
• decoupled generation
• octree-based vertex representation