← 研踪 PaperTrailCVF · CVPR 2026:cvf:4b511c237f3f84d58d3a7ee14578e8d5 · 记录 #1
MeshRipple: Structured Autoregressive Generation of Artist-Meshes
Lin, JunkaiLong, HangGuo, HuipengZhang, JieleiYang, JiayiGuo, TianleYang, YangLi, JianwenZHANG, WenxiaoNießner, MatthiasYang, Wei 来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 00:16
摘要
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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记录 #1 · 2026 · 最新观测版本
MeshRipple: Structured Autoregressive Generation of Artist-Meshes
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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