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PolyDiff: Generating 3D Polygonal Meshes with Diffusion Models

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arxiv 2312.11417 v1 pith:PF4W3ESV submitted 2023-12-18 cs.CV

Antonio Alliegro , Yawar Siddiqui , Tatiana Tommasi , Matthias Nie{ss}ner This is my paper · ORCID
classification cs.CV
keywords meshesdiffusionpolygonaldenoisingpolydiffapproachcapablegenerating
verification ladder T0 review T1 audit T2 compute T3 formal
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We introduce PolyDiff, the first diffusion-based approach capable of directly generating realistic and diverse 3D polygonal meshes. In contrast to methods that use alternate 3D shape representations (e.g. implicit representations), our approach is a discrete denoising diffusion probabilistic model that operates natively on the polygonal mesh data structure. This enables learning of both the geometric properties of vertices and the topological characteristics of faces. Specifically, we treat meshes as quantized triangle soups, progressively corrupted with categorical noise in the forward diffusion phase. In the reverse diffusion phase, a transformer-based denoising network is trained to revert the noising process, restoring the original mesh structure. At inference, new meshes can be generated by applying this denoising network iteratively, starting with a completely noisy triangle soup. Consequently, our model is capable of producing high-quality 3D polygonal meshes, ready for integration into downstream 3D workflows. Our extensive experimental analysis shows that PolyDiff achieves a significant advantage (avg. FID and JSD improvement of 18.2 and 5.8 respectively) over current state-of-the-art methods.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. LATO.2: Factorized 3D Mesh Generation with Vertex and Topology Flow

    cs.GR 2026-07 conditional novelty 6.0 of 10

    LATO.2 factorizes mesh generation into a vertex-generation flow and a vertex-conditioned connectivity flow, beating joint-latent and autoregressive baselines on geometric fidelity and connectivity quality.

  2. PoseAlign: Sculpting Pose-Consistent Meshes via Text-Guided Deformation

    cs.GR 2026-07 conditional novelty 6.0 of 10

    Two-stage text-guided mesh deformation (Laplacian CLIP scaling + attention-shared SDS Jacobian sculpting) better preserves source pose while aligning to text than TextDeformer or MeshUp.

  3. LL3M: Large Language 3D Modelers

    cs.GR 2025-08 conditional novelty 6.0 of 10

    A multi-agent LLM system generates editable 3D assets as Blender Python code, using documentation retrieval and visual self-critique to refine results.

  4. MeshLLM: Empowering Large Language Models to Progressively Understand and Generate 3D Mesh

    cs.GR 2025-08 unverdicted novelty 6.0 of 10

    MeshLLM improves LLM-based 3D mesh understanding and generation through primitive decomposition, a 1500k+ sample dataset, and topology-focused training strategies.

  5. RelMap: Reliable Spatiotemporal Sensor Data Visualization via Imputative Spatial Interpolation

    cs.LG 2025-08 unverdicted novelty 4.0 of 10

    RelMap combines GNN-based imputation with spatial interpolation and uncertainty-aware heatmaps for spatiotemporal sensor data.

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