Li, Baiyi;
Ho, Edmond SL;
Shum, Hubert PH;
Wang, He;
(2024)
Two-Person Interaction Augmentation with Skeleton Priors.
In:
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
(pp. pp. 1900-1910).
IEEE: Seattle, WA, USA.
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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.
| Type: | Proceedings paper |
|---|---|
| Title: | Two-Person Interaction Augmentation with Skeleton Priors |
| Event: | 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
| Location: | WA, Seattle |
| Dates: | 16 Jun 2024 - 22 Jun 2024 |
| ISBN-13: | 979-8-3503-6548-1 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/CVPRW63382.2024.00196 |
| Publisher version: | https://doi.org/10.1109/cvprw63382.2024.00196 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Deep learning, Computer vision, Conferences, Activity recognition, Animation, Skeleton, Motion capture |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10206677 |
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