改进骨骼生成的序列表示与测试时增强，提升精度与泛化能力
Branch-Centric Tokenization and Test-Time Augmentation for Skeleton Generation
- 采用分支中心化编码，将相关关节邻近排列，直接在序列中体现连接关系
- 测试时通过多视角旋转生成并融合结果，使骨骼覆盖更完整、预测更一致
- 在Articulation-XL2.0上比最强基线误差降低16.9%，适合复杂场景骨骼重建
自动骨骼生成需同时预测关节位置与连接关系。现有方法难以将分支结构融入序列表示，且未有效利用测试时计算。本文在统一自回归框架下提出两个改进：首先引入分支中心化标记法，将结构相关的元素相邻排列，并在序列中直接编码连接性，相比标准广度优先序列更紧凑；其次提出视图增强生成策略，对输入网格进行轴对齐旋转，将各视角预测映射回统一坐标系，根据网格覆盖率与预测一致性选择最优骨骼。实验表明，本方法在骨架预测精度上优于当前最优方法。尤其在Articulation-XL2.0数据集上，相比最强可比基线Auto-Connect，CD-J2B误差降低16.9%。在真实场景网格上的定性结果也显示良好的泛化能力。
原文摘要 · Abstract (English)
Automatic skeleton generation involves predicting both joint positions and skeletal connectivity. However, existing approaches struggle to encode branch structures into token sequences and do not use test-time computation effectively. We study these choices within a unified autoregressive framework. First, we introduce branch-centric tokenization, a branch-aware representation that places structurally related elements next to each other and encodes connectivity directly in the sequence. Compared with standard BFS-style serialization, this representation yields more compact sequences. Second, we introduce view-augmented generation, a test-time augmentation procedure that applies axis-aligned rotations to the input mesh, maps all predictions back to a common frame, and selects the final skeleton based on mesh coverage and consistency among predictions from different views. Experiments show that our method achieves better skeleton prediction accuracy than state-of-the-art methods. In particular, our method reduces the CD-J2B error by 16.9% on the Articulation-XL2.0 dataset compared to the strongest directly comparable baseline, Auto-Connect. Qualitative results on in-the-wild meshes further demonstrate generalization across diverse inputs.
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