Group dance generation from music requires synchronizing multiple dancers while maintaining spatial coordination, making it highly relevant to applications such as film production, gaming, and animation. Recent group dance generation models have achieved promising generation quality, but they remain difficult to deploy in interactive scenarios due to bidirectional attention dependencies. As the number of dancers and the sequence length increase, the attention computation required for aligning music conditions with motion sequences grows quadratically, leading to reduced efficiency and increased risk of motion collisions. Effectively modeling dense spatial-temporal interactions is therefore essential, yet existing methods often struggle to capture such complexity, resulting in limited scalability and unstable multi-dancer coordination. To address these challenges, we propose ST-GDance++, a scalable framework that decouples spatial and temporal dependencies to enable efficient and collision-aware group choreography generation. For spatial modeling, we introduce lightweight distance-aware graph convolutions to capture inter-dancer relationships while reducing computational overhead. For temporal modeling, we design a diffusion noise scheduling strategy together with an efficient temporal-aligned attention mask, enabling stream-based generation for long motion sequences and improving scalability in long-duration scenarios. Experiments on the AIOZ-GDance dataset show that ST-GDance++ achieves competitive generation quality with significantly reduced latency compared to existing methods.

翻译：从音乐生成群体舞蹈需要在保持空间协调的同时同步多个舞者，这使得该任务与电影制作、游戏和动画等应用高度相关。近期群体舞蹈生成模型已取得令人满意的生成质量，但由于双向注意力依赖，它们仍难以部署到交互场景中。随着舞者数量和序列长度的增加，对齐音乐条件与动作序列所需的注意力计算量呈二次方增长，导致效率降低并增加运动碰撞风险。因此，有效建模密集的时空交互至关重要，然而现有方法往往难以捕捉这种复杂性，导致可扩展性受限和多舞者协调不稳定。为解决这些挑战，我们提出ST-GDance++——一个解耦空间与时间依赖关系的可扩展框架，能够高效生成具有碰撞感知的群体编舞。在空间建模方面，我们引入轻量级距离感知图卷积来捕捉舞者间关系，同时降低计算开销。在时间建模方面，我们设计了扩散噪声调度策略及高效的时间对齐注意力掩码，支持长运动序列的流式生成，并提升长时间场景的可扩展性。在AIOZ-GDance数据集上的实验表明，与现有方法相比，ST-GDance++在显著降低延迟的同时实现了具有竞争力的生成质量。