发表机构
Aalto University（阿尔托大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文揭示跨骨骼运动重定向在标准生成目标下不可识别，提出源实例保真度（SIF）诊断，证明现有方法常忽略源信息，需新目标与评估。
AI 中文摘要
跨骨骼运动生成训练生成模型将动作结构和运动意图从一个身体传递到另一个身体。然而，一个展示正确动作的目标运动有两种训练数据无法区分的解释：模型转移了源片段，或者模型恢复了所请求动作的典型运动。我们表明这种模糊性是结构性的而非偶然的：在标准生成目标下，源条件重定向映射在稀疏异质运动域中是不可识别的。无配对分布匹配导致规范不可识别性：不同骨骼的潜在空间可以相对变换而不改变训练证据，因此不同的源条件映射同样适合。稀疏配对监督允许互补的失败模式，即条件均值退化：当片段仅按动作配对时，平方误差训练收敛到忽略源片段的平均目标运动。为了使缺失的证据可观察，我们引入了源实例保真度（SIF），这是一种诊断方法，测试输出是否像其源片段那样彼此不同，同时保持目标骨骼和动作固定。在此诊断下，在动物运动数据上通过标准动作级测试的方法通常处于源盲底限，而高于此底限的方法仅保留部分关系信号。因此，重定向需要能够识别其声称学习的源条件映射的目标和评估。项目页面：此 https URL。
英文摘要
Cross-skeleton motion generation trains generative models to carry action structure and motion intention from one body to another. Yet a target motion that shows the right action has two explanations that the training data cannot tell apart: the model transferred the source clip, or it recovered a typical motion for the requested action. We show that this ambiguity is structural rather than incidental: under standard generative objectives, the source-conditioned retargeting map is non-identifiable in sparse heterogeneous motion domains. Unpaired distribution matching yields gauge non-identifiability: the latent spaces of different skeletons can be transformed relative to one another without changing the training evidence, so different source-conditioned maps fit it equally well. Sparse paired supervision admits the complementary failure mode, \emph{conditional-mean degeneration}: when clips are paired only by action, squared-error training converges to an average target motion that ignores the source clip. To make the missing evidence observable, we introduce Source-Instance Fidelity (SIF), a diagnostic that tests whether outputs differ from one another the way their source clips do, with the target skeleton and action held fixed. Under this diagnostic, methods that succeed at the standard action-level test on animal motion data often sit at the source-blind floor, while the methods that rise above it retain only a partial relational signal. Retargeting therefore needs objectives and evaluations that can identify the source-conditioned map it claims to learn. Project page: https://cross-skeleton-retargeting.netlify.app/.
