Music-driven group choreography poses a considerable challenge but holds significant potential for a wide range of industrial applications. The ability to generate synchronized and visually appealing group dance motions that are aligned with music opens up opportunities in many fields such as entertainment, advertising, and virtual performances. However, most of the recent works are not able to generate high-fidelity long-term motions, or fail to enable controllable experience. In this work, we aim to address the demand for high-quality and customizable group dance generation by effectively governing the consistency and diversity of group choreographies. In particular, we utilize a diffusion-based generative approach to enable the synthesis of flexible number of dancers and long-term group dances, while ensuring coherence to the input music. Ultimately, we introduce a Group Contrastive Diffusion (GCD) strategy to enhance the connection between dancers and their group, presenting the ability to control the consistency or diversity level of the synthesized group animation via the classifier-guidance sampling technique. Through intensive experiments and evaluation, we demonstrate the effectiveness of our approach in producing visually captivating and consistent group dance motions. The experimental results show the capability of our method to achieve the desired levels of consistency and diversity, while maintaining the overall quality of the generated group choreography.

翻译：音乐驱动的群体编排是一项极具挑战性的任务，但在众多工业应用中具有重要潜力。生成与音乐同步且视觉上具有吸引力的群体舞蹈动作，为娱乐、广告和虚拟表演等领域开辟了新的机遇。然而，近期的大多数工作无法生成高保真度的长期动作，或未能实现可控体验。本研究旨在通过有效调控群体编排的一致性与多样性，满足高质量、可定制的群体舞蹈生成需求。具体而言，我们采用基于扩散的生成方法，在保证与输入音乐连贯性的同时，实现灵活数量的舞者合成及长期群体舞蹈生成。最终，我们引入群体对比扩散（Group Contrastive Diffusion, GCD）策略，以增强舞者与群体间的关联，并通过分类器引导采样技术，实现合成群体动画的一致性水平或多样性水平的可控调节。通过大量实验与评估，我们证明了该方法在生成视觉上引人入胜且连贯的群体舞蹈动作方面的有效性。实验结果显示，我们的方法能够在保持整体生成质量的前提下，达成预期的一致性与多样性水平。