We present CycleDance, a dance style transfer system to transform an existing motion clip in one dance style to a motion clip in another dance style while attempting to preserve motion context of the dance. Our method extends an existing CycleGAN architecture for modeling audio sequences and integrates multimodal transformer encoders to account for music context. We adopt sequence length-based curriculum learning to stabilize training. Our approach captures rich and long-term intra-relations between motion frames, which is a common challenge in motion transfer and synthesis work. We further introduce new metrics for gauging transfer strength and content preservation in the context of dance movements. We perform an extensive ablation study as well as a human study including 30 participants with 5 or more years of dance experience. The results demonstrate that CycleDance generates realistic movements with the target style, significantly outperforming the baseline CycleGAN on naturalness, transfer strength, and content preservation.

翻译：我们提出CycleDance，一种舞蹈风格迁移系统，旨在将一段已有舞蹈动作片段从一种风格转换为另一种风格，同时尽可能保留舞蹈的动作语境。该方法基于现有CycleGAN架构进行扩展，以建模音频序列，并集成了多模态Transformer编码器来考虑音乐语境。我们采用基于序列长度的课程学习策略以稳定训练过程。本方法能够捕获动作帧之间丰富且长期的内在关联，这是动作迁移与合成工作中普遍面临的挑战。我们进一步提出了用于衡量舞蹈动作迁移强度与内容保留程度的新型评价指标。通过全面的消融实验以及包含30名具有五年及以上舞蹈经验参与者的人类评估研究，结果表明CycleDance能够生成具有目标风格的真实动作，在自然度、迁移强度及内容保留方面显著优于基础CycleGAN模型。