Choreography refers to creation of dance steps and motions for dances according to the latent knowledge in human mind, where the created dance motions are in general style-specific and consistent. So far, such latent style-specific knowledge about dance styles cannot be represented explicitly in human language and has not yet been learned in previous works on music-to-dance generation tasks. In this paper, we propose a novel music-to-dance synthesis framework with controllable style embeddings. These embeddings are learned representations of style-consistent kinematic abstraction of reference dance clips, which act as controllable factors to impose style constraints on dance generation in a latent manner. Thus, the dance styles can be transferred to dance motions by merely modifying the style embeddings. To support this study, we build a large music-to-dance dataset. The qualitative and quantitative evaluations demonstrate the advantage of our proposed framework, as well as the ability of synthesizing diverse styles of dances from identical music via style embeddings.

中文翻译：

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嵌入样式的舞蹈生成：学习和转移舞蹈样式的潜在表示

编舞是指根据人脑中的潜在知识为舞蹈创建舞步和动作，其中所创建的舞动作通常是特定于样式且一致的。迄今为止，关于舞蹈风格的这种潜在的特定于风格的知识还不能用人类语言明确表示，并且在以前关于音乐到舞蹈生成任务的著作中还没有学到。在本文中，我们提出了一种具有可控风格嵌入的新颖的音乐至舞蹈合成框架。这些嵌入是参考舞蹈剪辑的样式一致的运动学抽象的学习表示，它们充当可控因素，以潜在的方式将样式约束强加于舞蹈生成。因此，仅通过修改样式嵌入，就可以将舞蹈样式转换为舞蹈动作。为了支持这项研究，我们建立了一个大型的音乐舞蹈数据集。定性和定量评估证明了我们提出的框架的优势，以及通过样式嵌入从同一首音乐中合成出多种舞蹈风格的能力。