Our paper on generating 3D dance moves from music (Music2Dance) is accepted to IEEE RA-L

[2020.02.06]

The following paper is accepted to the IEEE Robotics and Automation Letters (RA-L):

  • Generative Autoregressive Networks for 3D Dancing Move Synthesis from Music by Hyemin Ahn, Jaehun Kim, Kihyun Kim, and Songhwai Oh
    • Abstract: This paper 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.
    • Video