Synergy and Synchrony in Couple Dances
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909307570749440 |
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| author | Maluleke, Vongani Müller, Lea Rajasegaran, Jathushan Pavlakos, Georgios Ginosar, Shiry Kanazawa, Angjoo Malik, Jitendra |
| author_facet | Maluleke, Vongani Müller, Lea Rajasegaran, Jathushan Pavlakos, Georgios Ginosar, Shiry Kanazawa, Angjoo Malik, Jitendra |
| contents | This paper asks to what extent social interaction influences one's behavior. We study this in the setting of two dancers dancing as a couple. We first consider a baseline in which we predict a dancer's future moves conditioned only on their past motion without regard to their partner. We then investigate the advantage of taking social information into account by conditioning also on the motion of their dancing partner. We focus our analysis on Swing, a dance genre with tight physical coupling for which we present an in-the-wild video dataset. We demonstrate that single-person future motion prediction in this context is challenging. Instead, we observe that prediction greatly benefits from considering the interaction partners' behavior, resulting in surprisingly compelling couple dance synthesis results (see supp. video). Our contributions are a demonstration of the advantages of socially conditioned future motion prediction and an in-the-wild, couple dance video dataset to enable future research in this direction. Video results are available on the project website: https://von31.github.io/synNsync |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_04440 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Synergy and Synchrony in Couple Dances Maluleke, Vongani Müller, Lea Rajasegaran, Jathushan Pavlakos, Georgios Ginosar, Shiry Kanazawa, Angjoo Malik, Jitendra Computer Vision and Pattern Recognition This paper asks to what extent social interaction influences one's behavior. We study this in the setting of two dancers dancing as a couple. We first consider a baseline in which we predict a dancer's future moves conditioned only on their past motion without regard to their partner. We then investigate the advantage of taking social information into account by conditioning also on the motion of their dancing partner. We focus our analysis on Swing, a dance genre with tight physical coupling for which we present an in-the-wild video dataset. We demonstrate that single-person future motion prediction in this context is challenging. Instead, we observe that prediction greatly benefits from considering the interaction partners' behavior, resulting in surprisingly compelling couple dance synthesis results (see supp. video). Our contributions are a demonstration of the advantages of socially conditioned future motion prediction and an in-the-wild, couple dance video dataset to enable future research in this direction. Video results are available on the project website: https://von31.github.io/synNsync |
| title | Synergy and Synchrony in Couple Dances |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.04440 |