Nexus: Native Mesh Generation with Diffusion

• URL: http://arxiv.org/abs/2607.13563v1
• Date: Wed, 15 Jul 2026 08:04:46 GMT
• Title: Nexus: Native Mesh Generation with Diffusion
• Abstract summary: We present Nexus, a diffusion method that achieves holistic mesh generation via decoupled and topology generation.<n>Our method outperforms state-of-the-art autoregressive and two-stage baselines.<n>A blind user study from 3D practitioners confirms strong preference for our results.
• Score: 42.84943999520515
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Meshy T2: Fast Native Mesh Generation with Flow Matching [8.108033268825832]
  We present Meshy T2, a fast native mesh generation framework built on flow matching.<n>In experiments, Meshy T2 achieves state-of-the-art geometric fidelity and completes end-to-end image-to-mesh generation within a median of 6 seconds.
  arXiv  Detail & Related papers  (2026-07-28T16:33:54Z)
• PointDiT: Pixel-Space Diffusion for Monocular Geometry Estimation [105.86385617182879]
  State-of-the-art single-image 3D reconstruction methods often rely on complex hybrid architectures and loss functions.<n>We show that such architectural overhead and intricate loss formulations are unnecessary.<n>We introduce a minimalist pixel-space Diffusion Transformer, built on a plain ViT, that operates directly on raw 3D point map patches.
  arXiv  Detail & Related papers  (2026-07-02T17:59:56Z)
• TriFlow: Generating Artist-Like 3D Mesh Topology via Nearest-Vertex Vector Fields [72.76537377865192]
  TriFlow is a new generative approach for producing compact 3D meshes with artist-like triangle topology directly from input geometry conditions such as signed distance fields.<n>Experiments demonstrate that TriFlow achieves stronger generalization and significantly improved topology quality compared to state-of-the-art learning-based approaches.
  arXiv  Detail & Related papers  (2026-06-18T11:55:00Z)
• MeshWeaver: Sparse-Voxel-Guided Surface Weaving for Autoregressive Mesh Generation [53.06620176910962]
  MeshWeaver is an autoregressive framework that treats mesh generation as a surface weaving process.<n>MeshWeaver achieves a state-of-the-art compression ratio of 18%, can generate meshes with up to 16K faces, and significantly improves geometric fidelity over prior approaches.
  arXiv  Detail & Related papers  (2026-06-03T10:15:43Z)
• Attention-Guided Flow-Matching for Sparse 3D Geological Generation [7.734651455015875]
  3D-GeoFlow is the first Attention-Guided Continuous Flow Matching framework tailored for sparse multimodal geological modeling.<n>By reformulating discrete categorical generation as a simulation-free, continuous vector field regression, our model establishes stable, deterministic optimal transport paths.<n>To validate our framework, we curated a large-scale multimodal dataset comprising 2,200 procedurally generated 3D geological cases.
  arXiv  Detail & Related papers  (2026-04-07T07:02:53Z)
• Topology Sculptor, Shape Refiner: Discrete Diffusion Model for High-Fidelity 3D Meshes Generation [14.55646181682844]
  Topology Sculptor, Shape Refiner (TSSR) is a novel method for generating high-quality, artist-style 3D meshes.<n>We leverage this parallel generation capability through three key innovations.<n> experiments on complex datasets demonstrate that TSSR generates high-quality 3D artist-style meshes.
  arXiv  Detail & Related papers  (2025-10-24T08:51:48Z)
• MeshCraft: Exploring Efficient and Controllable Mesh Generation with Flow-based DiTs [79.45006864728893]
  MeshCraft is a framework for efficient and controllable mesh generation.<n>It uses continuous spatial diffusion to generate discrete triangle faces.<n>It can generate an 800-face mesh in just 3.2 seconds.
  arXiv  Detail & Related papers  (2025-03-29T09:21:50Z)
• DreamMesh4D: Video-to-4D Generation with Sparse-Controlled Gaussian-Mesh Hybrid Representation [10.250715657201363]
  We introduce DreamMesh4D, a novel framework combining mesh representation with geometric skinning technique to generate high-quality 4D object from a monocular video. Our method is compatible with modern graphic pipelines, showcasing its potential in the 3D gaming and film industry.
  arXiv  Detail & Related papers  (2024-10-09T10:41:08Z)
• MeshXL: Neural Coordinate Field for Generative 3D Foundation Models [51.1972329762843]
  We present a family of generative pre-trained auto-regressive models, which addresses the process of 3D mesh generation with modern large language model approaches. MeshXL is able to generate high-quality 3D meshes, and can also serve as foundation models for various down-stream applications.
  arXiv  Detail & Related papers  (2024-05-31T14:35:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.