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MeshArt: Generating Articulated Meshes with Structure-Guided Transformers

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arxiv 2412.11596 v2 pith:ZUHP3ZYZ submitted 2024-12-16 cs.CV cs.GR

MeshArt: Generating Articulated Meshes with Structure-Guided Transformers

classification cs.CV cs.GR
keywords meshpartarticulatedgenerationapproacharticulationmeshartmeshes
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Articulated 3D object generation is fundamental for creating realistic, functional, and interactable virtual assets which are not simply static. We introduce MeshArt, a hierarchical transformer-based approach to generate articulated 3D meshes with clean, compact geometry, reminiscent of human-crafted 3D models. We approach articulated mesh generation in a part-by-part fashion across two stages. First, we generate a high-level articulation-aware object structure; then, based on this structural information, we synthesize each part's mesh faces. Key to our approach is modeling both articulation structures and part meshes as sequences of quantized triangle embeddings, leading to a unified hierarchical framework with transformers for autoregressive generation. Object part structures are first generated as their bounding primitives and articulation modes; a second transformer, guided by these articulation structures, then generates each part's mesh triangles. To ensure coherency among generated parts, we introduce structure-guided conditioning that also incorporates local part mesh connectivity. MeshArt shows significant improvements over state of the art, with 57.1% improvement in structure coverage and a 209-point improvement in mesh generation FID.

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Cited by 2 Pith papers

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

  1. MeshFlow: Mesh Generation with Equivariant Flow Matching

    cs.GR 2026-06 unverdicted novelty 7.0

    MeshFlow applies equivariant optimal-transport flow matching to generate triangle meshes as soups, matching autoregressive quality with an 18x inference speedup.

  2. Learning to Build Shapes by Extrusion

    cs.GR 2026-01 unverdicted novelty 7.0

    Text Encoded Extrusions (TEE) lets LLMs generate and edit manifold 3D meshes by learning sequences of face extrusions from decomposed quadrilateral meshes.