  This paper presents an Exploratory 3D Dance generation framework, E3D2,
designed to address the exploration capability deficiency in existing
music-conditioned 3D dance generation models. Current models often generate
monotonous and simplistic dance sequences that misalign with human preferences
because they lack exploration capabilities. The E3D2 framework involves a
reward model trained from automatically-ranked dance demonstrations, which then
guides the reinforcement learning process. This approach encourages the agent
to explore and generate high quality and diverse dance movement sequences. The
soundness of the reward model is both theoretically and experimentally
validated. Empirical experiments demonstrate the effectiveness of E3D2 on the
AIST++ dataset. Project Page: https://sites.google.com/view/e3d2.
