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PivotMesh: Generic 3D Mesh Generation via Pivot Vertices Guidance

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arxiv 2405.16890 v1 pith:E5CQF37I submitted 2024-05-27 cs.CV

Haohan Weng , Yikai Wang , Tong Zhang , C. L. Philip Chen , Jun Zhu This is my paper · ORCID
classification cs.CV
keywords meshdatasetsmeshespivotmeshcompactgenerategenerationmodel
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Generating compact and sharply detailed 3D meshes poses a significant challenge for current 3D generative models. Different from extracting dense meshes from neural representation, some recent works try to model the native mesh distribution (i.e., a set of triangles), which generates more compact results as humans crafted. However, due to the complexity and variety of mesh topology, these methods are typically limited to small datasets with specific categories and are hard to extend. In this paper, we introduce a generic and scalable mesh generation framework PivotMesh, which makes an initial attempt to extend the native mesh generation to large-scale datasets. We employ a transformer-based auto-encoder to encode meshes into discrete tokens and decode them from face level to vertex level hierarchically. Subsequently, to model the complex typology, we first learn to generate pivot vertices as coarse mesh representation and then generate the complete mesh tokens with the same auto-regressive Transformer. This reduces the difficulty compared with directly modeling the mesh distribution and further improves the model controllability. PivotMesh demonstrates its versatility by effectively learning from both small datasets like Shapenet, and large-scale datasets like Objaverse and Objaverse-xl. Extensive experiments indicate that PivotMesh can generate compact and sharp 3D meshes across various categories, highlighting its great potential for native mesh modeling.

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Cited by 12 Pith papers

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

  1. VertexRegen: Mesh Generation with Continuous Level of Detail

    cs.GR 2025-08 conditional novelty 7.0 of 10

    VertexRegen generates meshes by autoregressively predicting vertex splits that refine a coarse base mesh, so every intermediate step is a valid mesh at a different level of detail.

  2. Hunyuan3D-Buffalo 1.0: A Unified Multimodal Model for Scalable 3D Generation, Understanding, and Editing

    cs.CV 2026-08 conditional novelty 6.0 of 10

    A unified 3D multimodal model combines understanding, text-to-3D generation, instruction-guided editing, and part generation in one architecture, trained on an 87M-sample corpus, with claimed state-of-the-art results.

  3. Nexus: Native Mesh Generation with Diffusion

    cs.CV 2026-07 conditional novelty 6.0 of 10

    Nexus replaces autoregressive mesh serialization with two coupled diffusion models — octree vertex generation and a latent topology generator — claiming stronger geometry and perceptual quality on Objaverse and Toys4K.

  4. 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.

  5. 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.

  6. Sat2City: 3D City Generation from A Single Satellite Image with Cascaded Latent Diffusion

    cs.CV 2025-07 conditional novelty 6.0 of 10

    Sat2City generates explicit 3D city geometry and appearance from a height-map condition using cascaded latent diffusion on sparse voxel grids, beating prior methods on a new synthetic city dataset.

  7. Efficient Part-level 3D Object Generation via Dual Volume Packing

    cs.CV 2025-06 conditional novelty 6.0 of 10

    From a single image, a 3D latent diffusion model generates all parts of an object at once by packing the part structure into two non-overlapping volumes.

  8. MeshArt: Generating Articulated Meshes with Structure-Guided Transformers

    cs.CV 2024-12 conditional novelty 6.0 of 10

    A hierarchical transformer that tokenizes triangles generates articulated 3D meshes part-by-part, with 57.1% higher structure coverage and a 209-point lower mesh FID than prior methods on an expanded PartNet.

  9. Meshtron: High-Fidelity, Artist-Like 3D Mesh Generation at Scale

    cs.GR 2024-12 conditional novelty 6.0 of 10

    Meshtron autoregressively generates 3D meshes with up to 64K faces at 1024-level coordinate resolution, a large scale increase over prior work, using an hourglass transformer and sliding-window inference.

  10. LLaMA-Mesh: Unifying 3D Mesh Generation with Language Models

    cs.LG 2024-11 conditional novelty 6.0 of 10

    Fine-tuning LLaMA-3.1-8B on an OBJ-as-text dataset lets one chat model both answer questions and generate simple 3D meshes, with no vocabulary expansion.

  11. ShapeLLM-Omni: A Native Multimodal LLM for 3D Generation and Understanding

    cs.CV 2025-06 conditional novelty 5.0 of 10

    ShapeLLM-Omni unifies text, image, and 3D generation and understanding in one autoregressive LLM using discrete 3D tokens and a new 3D-Alpaca training dataset.

  12. FreeMesh: Boosting Mesh Generation with Coordinates Merging

    cs.GR 2025-05 conditional novelty 5.0 of 10

    FreeMesh shows that rearranging coordinates into same-axis groups before byte-pair encoding reduces per-token entropy and sequence length, improving point-cloud conditioned mesh generation quality.

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