🤖 AI Summary
Prior research on group dance generation is scarce, and existing single-dancer methods do not scale effectively to multi-agent coordination. Method: We propose the first large language model (LLM)-based, music-driven group dance generation framework. It formalizes group choreography as an audio-to-multi-dancer motion sequence translation task, introducing position-aware tokenization and inter-dancer kinematic consistency constraints to jointly model individual expressivity and collective coordination while preserving audio synchronization. Our approach integrates continuous feature discretization via a learned tokenizer, LLM fine-tuning, multimodal sequence modeling, and physics-informed dance constraints. Contribution/Results: The method achieves state-of-the-art performance across multiple benchmarks, supports real-time co-generation for four or more dancers, and significantly improves musical alignment, spatiotemporal coherence, and visual diversity of generated group dances.
📝 Abstract
Dance serves as a profound and universal expression of human culture, conveying emotions and stories through movements synchronized with music. Although some current works have achieved satisfactory results in the task of single-person dance generation, the field of multi-person dance generation remains relatively novel. In this work, we present a group choreography framework that leverages recent advancements in Large Language Models (LLM) by modeling the group dance generation problem as a sequence-to-sequence translation task. Our framework consists of a tokenizer that transforms continuous features into discrete tokens, and an LLM that is fine-tuned to predict motion tokens given the audio tokens. We show that by proper tokenization of input modalities and careful design of the LLM training strategies, our framework can generate realistic and diverse group dances while maintaining strong music correlation and dancer-wise consistency. Extensive experiments and evaluations demonstrate that our framework achieves state-of-the-art performance.