mlsanityData to 7 Oct 2026

MeshRipple: Structured Autoregressive Generation of Artist-Meshes

arXiv 2512.07514 ↗First posted 8 Dec 2025cs.CVcited by 8

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Abstract (arXiv)

Meshes serve as a primary representation for 3D assets. Autoregressive mesh generators serialize faces into sequences and train on truncated segments with sliding-window inference to cope with memory limits. However, this mismatch breaks long-range geometric dependencies, producing holes and fragmented components. To address this critical limitation, we introduce MeshRipple, which expands a mesh outward from an active generation frontier, akin to a ripple on a surface. MeshRipple rests on three key innovations: a frontier-aware BFS tokenization that aligns the generation order with surface topology; an expansive prediction strategy that maintains coherent, connected surface growth; and a sparse-attention global memory that provides an effectively unbounded receptive field to resolve long-range topological dependencies. This integrated design enables MeshRipple to generate meshes with high surface fidelity and topological completeness, outperforming strong recent baselines.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 80 of its 87 references that are in the map. First posted on arXiv 8 Dec 2025.

Builds on

80 of 87 references.

References · 80

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38 more
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Citing papers per year: 2026: 8.

8 papers.