  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.
