论文标题
舞蹈风格转移与跨模式变压器
Dance Style Transfer with Cross-modal Transformer
论文作者
论文摘要
我们提出了循环式的舞蹈风格转移系统，该系统将以一种舞蹈方式将现有的运动剪辑转换为另一种舞蹈风格的运动剪辑，同时试图保留舞蹈的运动背景。我们的方法扩展了一个现有的自行车结构，用于建模音频序列并集成多模式变压器编码器以说明音乐上下文。我们采用基于序列长度的课程学习来稳定培训。我们的方法捕获了运动框架之间丰富而长期的内部关系，这是运动转移和合成工作中的普遍挑战。我们进一步介绍了在舞蹈运动的背景下衡量转移力量和内容保存的新指标。我们进行了一项广泛的消融研究以及一项人类研究，其中包括30名具有5年或更长时间的舞蹈经验的参与者。结果表明，循环产生了目标样式的现实运动，在自然性，传递强度和内容保存方面的基线周期大大优于基线周期。
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.
