MeshRipple: Structured Autoregressive Generation of Artist-Meshes

• URL: http://arxiv.org/abs/2512.07514v2
• Date: Tue, 09 Dec 2025 09:28:24 GMT
• Title: MeshRipple: Structured Autoregressive Generation of Artist-Meshes
• Authors: Junkai Lin, Hang Long, Huipeng Guo, Jielei Zhang, JiaYi Yang, Tianle Guo, Yang Yang, Jianwen Li, Wenxiao Zhang, Matthias Nießner, Wei Yang,
• Abstract summary: Meshes serve as a primary representation for 3D assets.<n>We introduce MeshRipple, which expands a mesh outward from an active generation frontier, akin to a ripple on a surface.<n>This integrated design enables MeshRipple to generate meshes with high surface fidelity and topological completeness, outperforming strong recent baselines.
• Score: 43.29985039393799
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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