  Close and continuous interaction with rich contacts is a crucial aspect of
human activities (e.g. hugging, dancing) and of interest in many domains like
activity recognition, motion prediction, character animation, etc. However,
acquiring such skeletal motion is challenging. While direct motion capture is
expensive and slow, motion editing/generation is also non-trivial, as complex
contact patterns with topological and geometric constraints have to be
retained. To this end, we propose a new deep learning method for two-body
skeletal interaction motion augmentation, which can generate variations of
contact-rich interactions with varying body sizes and proportions while
retaining the key geometric/topological relations between two bodies. Our
system can learn effectively from a relatively small amount of data and
generalize to drastically different skeleton sizes. Through exhaustive
evaluation and comparison, we show it can generate high-quality motions, has
strong generalizability and outperforms traditional optimization-based methods
and alternative deep learning solutions.
