Two-Person Interaction Augmentation with Skeleton Priors
This work addresses a domain-specific problem in motion capture and animation by providing an efficient method for augmenting interaction data, though it is incremental as it builds on existing deep learning approaches.
The paper tackles the challenge of generating variations of contact-rich two-person skeletal interactions with different body sizes while preserving geometric and topological constraints, achieving high-quality motion generation that outperforms traditional optimization-based and deep learning methods.
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.