Two-Person Interaction Augmentation with Skeleton Priors

Baiyi Li, Edmond S. L. Ho, Hubert P. H. Shum, He Wang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 1900-1910

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.