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Duolando: Follower GPT with Off-Policy Reinforcement Learning for Dance Accompaniment

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arxiv 2403.18811 v1 pith:NB373ED6 submitted 2024-03-27 cs.CV cs.GRcs.SDeess.AS

Li Siyao , Tianpei Gu , Zhitao Yang , Zhengyu Lin , Ziwei Liu , Henghui Ding , Lei Yang , Chen Change Loy This is my paper · ORCID
classification cs.CVcs.GRcs.SDeess.AS
keywords dancefollowergenerationmovementstaskaccompanimentdatasetduet
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We introduce a novel task within the field of 3D dance generation, termed dance accompaniment, which necessitates the generation of responsive movements from a dance partner, the "follower", synchronized with the lead dancer's movements and the underlying musical rhythm. Unlike existing solo or group dance generation tasks, a duet dance scenario entails a heightened degree of interaction between the two participants, requiring delicate coordination in both pose and position. To support this task, we first build a large-scale and diverse duet interactive dance dataset, DD100, by recording about 117 minutes of professional dancers' performances. To address the challenges inherent in this task, we propose a GPT-based model, Duolando, which autoregressively predicts the subsequent tokenized motion conditioned on the coordinated information of the music, the leader's and the follower's movements. To further enhance the GPT's capabilities of generating stable results on unseen conditions (music and leader motions), we devise an off-policy reinforcement learning strategy that allows the model to explore viable trajectories from out-of-distribution samplings, guided by human-defined rewards. Based on the collected dataset and proposed method, we establish a benchmark with several carefully designed metrics.

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Cited by 7 Pith papers

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

  1. 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.

  2. InterPet4D: A Multimodal 4D Human-Pet Interaction Dataset for Pet Motion Generation

    cs.CV 2026-07 conditional novelty 6.5 of 10

    A first large multimodal 4D human–dog interaction dataset (6.8M frames) plus an autoregressive model that generates dog motion from human body/hand gestures and audio.

  3. Wan-Dancer: A Hierarchical Framework for Minute-scale Coherent Music-to-Dance Generation

    cs.CV 2026-07 conditional novelty 6.0 of 10

    A hierarchical global-keyframe then local-refinement diffusion pipeline produces stable 720p/30fps music-to-dance videos longer than one minute across five genres.

  4. Real-time and Controllable Reactive Motion Synthesis via Intention Guidance

    cs.GR 2025-07 conditional novelty 6.0 of 10

    A neural system predicts key-joint intentions from motion history and uses adversarially regularized codebook matching to synthesize controllable, real-time reactive motions.

  5. FlowerDance: MeanFlow for Efficient and Refined 3D Dance Generation

    cs.CV 2025-11 conditional novelty 5.0 of 10

    FlowerDance pairs MeanFlow few-step flow matching with a bidirectional Mamba backbone and physical-consistency losses, reporting state-of-the-art dance quality at 2008 FPS on FineDance and AIST++.

  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.

  7. 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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