MeshRipple: 结构化自回归生成艺术家网格
MeshRipple: Structured Autoregressive Generation of Artist-Meshes
AI总结：
MeshRipple通过结构化自回归生成方法，实现高保真度和拓扑完整的艺术家网格生成。
摘要：
网格是3D资产的主要表示形式。自回归网格生成器将面序列化并训练于截断段，通过滑动窗口推理来应对内存限制。然而，这种不匹配会破坏长距离几何依赖关系，产生孔洞和碎片化组件。为解决这一关键限制，我们引入MeshRipple，它从活跃生成前沿向外扩展网格，如同表面上的涟漪。MeshRipple基于三个关键创新：一种前沿感知的BFS标记化，使生成顺序与表面拓扑对齐；一种扩展预测策略，保持连贯且连接的表面增长；以及一种稀疏注意力全局内存，提供有效无界的接收场以解决长距离拓扑依赖。这种集成设计使MeshRipple能够生成高表面保真度和拓扑完整性的网格，优于强大的近期基线。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
