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.

翻译：本文提出了一种探索性三维舞蹈生成框架E3D2，旨在解决现有音乐条件三维舞蹈生成模型探索能力不足的问题。当前模型常生成单调简单的舞蹈序列，与人类偏好不符，根本原因在于缺乏探索能力。E3D2框架包含一个基于自动排序舞蹈示范训练的奖励模型，该模型进一步引导强化学习过程。该方法促使智能体探索并生成高质量、多样化的舞蹈动作序列。我们从理论和实验两个层面验证了奖励模型的合理性。在AIST++数据集上的实证实验证明了E3D2的有效性。项目主页：https://sites.google.com/view/e3d2。