Cepheid：解耦尺度与结构的跨几何家族运动补全
Cepheid: Decoupling Scale from Structure for Cross-Family Motion Completion
这篇 Cepheid 论文让运动补全跨布料、四足等几何家族通用，零重训就能迁移到动物视频和机器人抓取。
arXiv 论文 Cepheid 研究稀疏到稠密的运动补全：从稀疏顶点位移恢复稠密网格运动。在单一几何家族上训练的残差先验，跨家族使用时常打不过无参数的 harmonic extension。跨家族训练存在尺度障碍：残差幅值在不同几何家族间相差超过一个数量级，且随观测变密而坍缩，测试时无法直接测量。Cepheid 学习无量纲先验，把残差结构与物理尺度解耦，幅值由观测量加仅在训练源上拟合的轻量修正来估计。统一模型在所有评估家族（含未见过的布料和四足身体）上超过 harmonic extension，同一先验无需重训即可用于点跟踪动物视频和机器人抓取软体，不依赖骨架、配准或跨网格对应。
Cepheid: Decoupling Scale from Structure for Cross-Family Motion Completion
Sparse-to-dense motion completion recovers dense mesh motion from sparse vertex displacements. Residual priors trained on one geometry family frequently fail to beat parameter-free harmonic extension off-family. Moreover, cross-family training meets a scaling barrier: residual amplitude varies by more than an order of magnitude across geometry families and collapses as observations become denser. Since this scale depends on unknown target motion, it cannot be measured directly at test time. We decouple residual structure from physical scale by learning a dimensionless prior, estimating amplitude from observables and a lightweight correction fitted only on training sources. Our unified model surpasses harmonic extension across all evaluated families, including unseen cloth and legged bodies, without skeletons, registration, or cross-mesh correspondence. The same prior applies without retraining to point-tracked animal video and a robot grasping a soft body. Project page: https://su-terry.github.io/cepheid-website/
