mlsanityData to 7 Oct 2026

DanceDuo: Bridging Human Movement and AI Choreography

arXiv 2606.26507 ↗First posted 25 Jun 2026cs.HC cs.CVcited by 0

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Abstract (arXiv)

In recent years, advancements in deep learning and generative models have revolutionized music-driven dance generation. This paper introduces a novel platform, namely DanceDuo, leveraging diffusion models to generate AI-choreographed dance sequences synchronized with a variety of music genres, to encourage dancing practice. The system allows users to interact with AI by selecting music tracks, humanoid models, and importing personal dance videos for comparison, fostering a rich and engaging user experience. DanceDuo not only offers dance generation but also integrates human pose estimation models to provide users with insightful comparisons of their own performances with AI-generated sequences. We conducted a comprehensive user study, revealing that users found the interface intuitive, with particular praise for the dance comparison feature. Our DanceDuo contributes significantly to the integration of AI in dance choreography, offering novel avenues for both recreational and professional applications.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 40 of its 65 references that are in the map. First posted on arXiv 25 Jun 2026.

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40 of 65 references.

References · 40

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
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28 more
  1. 2020DiffWave: A Versatile Diffusion Model for Audio Synthesiscited by 1,339
  2. 2021Palette: Image-to-Image Diffusion Modelscited by 1,296
  3. 2022Diffusion-LM Improves Controllable Text Generationcited by 1,006
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  6. 2025Generative human motion mimicking through feature extraction in denoising diffusion settings
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