Generating co-speech gestures in real time requires both temporal coherence and efficient sampling. We introduce a novel framework for streaming gesture generation that extends Rolling Diffusion models with structured progressive noise scheduling, enabling seamless long-sequence motion synthesis while preserving realism and diversity. Our framework is universally compatible with existing diffusion-based gesture generation model, transforming them into streaming methods capable of continuous generation without requiring post-processing. We evaluate our framework on ZEGGS and BEAT, strong benchmarks for real-world applicability. Applied to state-of-the-art baselines on both datasets, it consistently outperforms them, demonstrating its effectiveness as a generalizable and efficient solution for real-time co-speech gesture synthesis. We further propose Rolling Diffusion Ladder Acceleration (RDLA), a new approach that employs a ladder-based noise scheduling strategy to simultaneously denoise multiple frames. This significantly improves sampling efficiency while maintaining motion consistency, achieving up to a 4x speedup with high visual fidelity and temporal coherence in our experiments. Comprehensive user studies further validate our framework ability to generate realistic, diverse gestures closely synchronized with the audio input.

翻译：实时生成伴随语音手势需要同时满足时序连贯性和高效采样。我们提出了一种新颖的流式手势生成框架，该框架通过结构化渐进噪声调度扩展了滚动扩散模型，能够在保持真实性和多样性的同时实现无缝的长序列动作合成。我们的框架与现有基于扩散的手势生成模型普遍兼容，可将其转化为无需后处理即能持续生成的流式方法。我们在ZEGGS和BEAT这两个具有实际应用价值的强基准数据集上评估了该框架。应用于两个数据集上的最先进基线模型时，本框架均表现出更优性能，证明了其作为实时伴随语音手势合成的通用高效解决方案的有效性。我们进一步提出了滚动扩散阶梯加速（RDLA），这是一种采用阶梯式噪声调度策略同时去噪多帧的新方法。该方法在保持动作一致性的同时显著提升了采样效率，在实验中实现了高达4倍的加速，且具有高视觉保真度和时序连贯性。全面的用户研究进一步验证了本框架能够生成与音频输入紧密同步的真实、多样化的手势。