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Synergy and Synchrony in Couple Dances

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arxiv 2409.04440 v1 pith:Z3TQG7BA submitted 2024-09-06 cs.CV

Vongani Maluleke , Lea Müller , Jathushan Rajasegaran , Georgios Pavlakos , Shiry Ginosar , Angjoo Kanazawa , Jitendra Malik This is my paper · ORCID
classification cs.CV
keywords couplefuturemotionvideodancepredictionbehaviorconditioned
verification ladder T0 review T1 audit T2 compute T3 formal
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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

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. CoMPAS3D: A Dataset and Benchmark for Interactive Motion

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    CoMPAS3D is a three-hour improvised salsa motion capture dataset with 2,800 expert move annotations across three proficiency levels, plus benchmarks showing that follower generation models produce motion that is not l...

  2. GNOCHI: Generative Neural mOdel for Close Human-Human Interactions

    cs.CV 2026-07 conditional novelty 6.5 of 10

    A pose-conditioned cVAE plus automated interaction data augmentation and a capsule-based collision-fix module synthesizes diverse, contact-preserving, non-penetrating 3D human-human poses better than prior generative ...

  3. Wave physics as a choreographic notation for partner dance

    physics.bio-ph 2026-04 unverdicted novelty 6.0 of 10

    Wave physics models partner dance movements as oscillatory waves exhibiting interference and harmonics, serving as a choreographic notation with acoustic analogies to musical dyads.

  4. DanceTogether! Identity-Preserving Multi-Person Interactive Video Generation

    cs.CV 2025-05 conditional novelty 6.0 of 10

    A diffusion model fuses per-person masks with pose keypoints to generate identity-preserving, two-person interactive videos from a single reference image, outperforming prior single-person-animation pipelines.

  5. Poly-Autoregressive Prediction for Modeling Interactions

    cs.CV 2025-02 conditional novelty 4.0 of 10

    A single transformer training recipe, poly-autoregressive prediction, improves multi-agent ego forecasting over autoregressive baselines on three distinct tasks.

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