双人舞中的协同与同步
Synergy and Synchrony in Couple Dances
AI总结：
该研究通过单人基线与舞伴条件预测的对比，证明社会互动能显著提升 Swing 双人舞未来动作预测与合成质量，并发布真实场景双人舞视频数据集。
摘要：
本文探讨社会互动在多大程度上影响一个人的行为。我们以两名舞者作为一对舞伴跳舞的场景来研究这一问题。我们首先考虑一个基线：仅根据一名舞者过去的动作预测其未来动作，而不考虑其舞伴。随后，我们研究将社会信息纳入考虑所带来的优势，即同时以其舞伴的动作为条件进行预测。我们将分析重点放在 Swing（摇摆舞）上，这是一种具有紧密身体耦合的舞蹈类型，并为此提出了一个真实场景视频数据集。我们证明，在这种情境下进行单人未来动作预测具有挑战性。相反，我们观察到，考虑互动舞伴的行为会显著改善预测，并产生出奇引人入胜的双人舞合成结果（见补充视频）。我们的贡献包括展示了社会条件化未来动作预测的优势，以及一个可推动该方向未来研究的真实场景双人舞视频数据集。视频结果可在项目网站获取：https://von31.github.io/synNsync
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
