Abstract
This letter proposes a framework which is able to generate a sequence of three-dimensional human dance poses for a given music. The proposed framework consists of three components: A music feature encoder, a pose generator, and a music genre classifier. We focus on integrating these components for generating a realistic 3D human dancing move from music, which can be applied to artificial agents and humanoid robots. The trained dance pose generator, which is a generative autoregressive model, is able to synthesize a dance sequence longer than 1,000 pose frames. Experimental results of generated dance sequences from various songs show how the proposed method generates human-like dancing move to a given music. In addition, a generated 3D dance sequence is applied to a humanoid robot, showing that the proposed framework can make a robot to dance just by listening to music.
Original language | English |
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Article number | 9019619 |
Pages (from-to) | 3501-3508 |
Number of pages | 8 |
Journal | IEEE Robotics and Automation Letters |
Volume | 5 |
Issue number | 2 |
DOIs | |
Publication status | Published - 1 Apr 2020 |
Keywords
- entertainment robotics
- Gesture
- novel deep learning methods
- posture and facial expressions