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InterDance:Reactive 3D Dance Generation with Realistic Duet Interactions

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arxiv 2412.16982 v1 pith:QEXHRFU6 submitted 2024-12-22 cs.CV cs.GRcs.MMcs.SDeess.AS

Ronghui Li , Youliang Zhang , Yachao Zhang , Yuxiang Zhang , Mingyang Su , Jie Guo , Ziwei Liu , Yebin Liu
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Xiu Li
This is my paper · ORCID
classification cs.CVcs.GRcs.MMcs.SDeess.AS
keywords dancemotionduetinteractionsinteractivedatasethandhigh-quality
verification ladder T0 review T1 audit T2 compute T3 formal
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Humans perform a variety of interactive motions, among which duet dance is one of the most challenging interactions. However, in terms of human motion generative models, existing works are still unable to generate high-quality interactive motions, especially in the field of duet dance. On the one hand, it is due to the lack of large-scale high-quality datasets. On the other hand, it arises from the incomplete representation of interactive motion and the lack of fine-grained optimization of interactions. To address these challenges, we propose, InterDance, a large-scale duet dance dataset that significantly enhances motion quality, data scale, and the variety of dance genres. Built upon this dataset, we propose a new motion representation that can accurately and comprehensively describe interactive motion. We further introduce a diffusion-based framework with an interaction refinement guidance strategy to optimize the realism of interactions progressively. Extensive experiments demonstrate the effectiveness of our dataset and algorithm.

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Cited by 6 Pith papers

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

  1. Learning to Generate Human-Human-Object Interactions from Textual Descriptions

    cs.CV 2025-11 conditional novelty 7.0 of 10

    A new dataset and score-based diffusion framework generate text-conditioned 3D interactions between multiple people and a shared object.

  2. MDD: A Dataset for Text-and-Music Conditioned Duet Dance Generation

    cs.GR 2025-08 conditional novelty 7.0 of 10

    MDD is the first dataset to pair text, music, and 3D duet dance motion, enabling two new text-conditioned duet generation tasks.

  3. CoMPAS3D: A Dataset and Benchmark for Interactive Motion

    cs.LG 2025-07 conditional novelty 7.0 of 10

    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...

  4. VolumetricSMPL: A Neural Volumetric Body Model for Efficient Interactions, Contacts, and Collisions

    cs.CV 2025-06 conditional novelty 6.0 of 10

    A new SMPL-compatible signed distance field body model, VolumetricSMPL, uses neural blend weights to cut inference time and memory about 10x and 6x versus COAP while matching or improving accuracy.

  5. PhysiInter: Integrating Physical Mapping for High-Fidelity Human Interaction Generation

    cs.CV 2025-06 conditional novelty 6.0 of 10

    A text-to-motion pipeline that projects motions through physics-based imitation for training and post-processing, plus new consistency and marker-interaction losses.

  6. Towards Immersive Human-X Interaction: A Real-Time Framework for Physically Plausible Motion Synthesis

    cs.CV 2025-08 unverdicted novelty 5.0 of 10

    Human-X jointly predicts actions and reactions in real time to produce physically plausible human-machine interaction motion.

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