Two-Person Interaction Augmentation with Skeleton Priors

Li, Baiyi; Ho, Edmond S. L.; Shum, Hubert P. H.; Wang, He

Abstract

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.

Citation

Li, B., Ho, E. S. L., Shum, H. P. H., & Wang, H. (2024, June). Two-Person Interaction Augmentation with Skeleton Priors. Presented at 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, Washington

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
• Start Date: Jun 17, 2024
• End Date: Jun 18, 2024
• Acceptance Date: Apr 6, 2024
• Online Publication Date: Sep 27, 2024
• Publication Date: Sep 27, 2024
• Deposit Date: Apr 17, 2024
• Publicly Available Date: Oct 9, 2024
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Pages: 1900-1910
• Series ISSN: 2160-7508
• Book Title: 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
• DOI: https://doi.org/10.1109/CVPRW63382.2024.00196
• Public URL: https://durham-repository.worktribe.com/output/2387053