ST-GDance++: A Scalable Spatial-Temporal Diffusion for Long-Duration Group Choreography

Abstract

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.

Figures (6)

• Figure 1: Comparison between vanilla Group Dance Transformer (top) and ours. We decouple the long-duration group dance sequence $N \times L$ into spatial and temporal parts to reduce the computational complexity to $O(LN^2)+O(NL^2)$. The overall complexity can be reduced into near $O(NL)$ (see Section \ref{['tt']}). Also in our framework, the temporal dimension is divided into segments and the noise schedule in training will also change to capture local dependencies and support streaming generation (will be discussed in Section \ref{['tns']}).
• Figure 2: Overview of the proposed ST-GDance++ framework for efficient long-duration group dance generation. (a) Overall generation architecture, where noised group motion is processed by the spatial–temporal diffusion decoder to produce the final multi-dancer sequence. (b) Spatial distance-aware graph modeling, which constructs a weighted graph based on pairwise dancer distances and applies a lightweight GCN to capture inter-dancer interactions. (c) Temporal transformer module, responsible for modeling long-range motion dynamics under music and timestep conditions. (d) Alignment attention mask (AAM), which enforces temporally consistent interactions across segments to support stable streaming generation.
• Figure 3: Noise schedule comparison. Diffusion Forcing typically samples training schedules with stochastic active windows, leading to a noticeable discrepancy between training and inference. To bridge this gap, we introduce a segment-based triangular schedule that exclusively denoises the current active window before advancing. By applying a consistent noise level to all frames within a single segment, our approach ensures temporal coherence and enhances the stability of long-duration generation.
• Figure 4: Qualitative results compared with baselines.
• Figure 5: Qualitative results generated by ST-GDance++ across varying group sizes. We demonstrate the generated group choreographies with different numbers of dancers, ranging from 2 to 5.