用AI生成配乐舞蹈，还能对比自己跳舞效果
DanceDuo: Bridging Human Movement and AI Choreography
- 基于扩散模型生成与音乐同步的舞蹈动作
- 支持用户上传视频对比自身与AI舞蹈表现
- 界面友好，适合健身、舞蹈爱好者使用
近年来，深度学习与生成模型的发展推动了音乐驱动舞蹈生成的革新。本文提出DanceDuo平台，利用扩散模型生成与多种音乐风格同步的AI choreographed 舞蹈序列，以鼓励舞蹈练习。用户可通过选择音乐曲目、人形模型或上传个人舞蹈视频进行对比，提升互动体验。DanceDuo还集成人体姿态估计模型，提供用户自身表演与AI生成序列的可视化对比。我们开展了一项全面的用户研究，结果显示用户认为界面直观，尤其赞赏舞蹈对比功能。DanceDuo显著促进了AI在舞蹈编排中的应用，为休闲与专业场景开辟新路径。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。