In the character animation field, modern supervised keyframe interpolation models have demonstrated exceptional performance in constructing natural human motions from sparse pose definitions. As supervised models, large motion datasets are necessary to facilitate the learning process; however, since motion is represented with fixed hierarchical skeletons, such datasets are incompatible for skeletons outside the datasets' native configurations. Consequently, the expected availability of a motion dataset for desired skeletons severely hinders the feasibility of learned interpolation in practice. To combat this limitation, we propose Point Cloud-based Motion Representation Learning (PC-MRL), an unsupervised approach to enabling cross-compatibility between skeletons for motion interpolation learning. PC-MRL consists of a skeleton obfuscation strategy using temporal point cloud sampling, and an unsupervised skeleton reconstruction method from point clouds. We devise a temporal point-wise K-nearest neighbors loss for unsupervised learning. Moreover, we propose First-frame Offset Quaternion (FOQ) and Rest Pose Augmentation (RPA) strategies to overcome necessary limitations of our unsupervised point cloud-to-skeletal motion process. Comprehensive experiments demonstrate the effectiveness of PC-MRL in motion interpolation for desired skeletons without supervision from native datasets.

翻译：在角色动画领域，现代有监督关键帧插值模型在从稀疏姿态定义中构建自然人体运动方面展现出卓越性能。作为有监督模型，大规模运动数据集对促进学习过程至关重要；然而，由于运动采用固定层级骨骼表示，此类数据集无法兼容其原生配置之外的骨骼结构。因此，目标骨骼所需运动数据集的预期可用性严重制约了学习式插值在实际中的应用可行性。为突破这一限制，我们提出基于点云的运动表示学习（PC-MRL）——一种无监督方法，旨在实现骨骼间运动插值学习的跨兼容性。PC-MRL包含两项核心策略：利用时间点云采样的骨骼混淆策略，以及基于点云的无监督骨骼重建方法。我们设计了时间逐点K近邻损失函数用于无监督学习。此外，我们提出首帧偏移四元数（FOQ）和静止姿态增强（RPA）策略，以克服无监督点云到骨骼运动过程中的关键限制。大量实验证明，PC-MRL能在无需原生数据集监督的情况下，有效实现对目标骨骼的运动插值。