To generate dance that temporally and aesthetically matches the music is a challenging problem in three aspects.First,the generated motion should be beats-aligned to the local musical features.Second,the global aesthe...To generate dance that temporally and aesthetically matches the music is a challenging problem in three aspects.First,the generated motion should be beats-aligned to the local musical features.Second,the global aesthetic style should be matched between motion and music.And third,the generated motion should be diverse and non-self-repeating.To address these challenges,we propose ReChoreoNet,which re-choreographs high-quality dance motion for a given piece of music.A data-driven learning strategy is proposed to efficiently correlate the temporal connections between music and motion in a progressively learned cross-modality embedding space.The beats-aligned content motion will be subsequently used as autoregressive context and control signal to control a normalizing-flow model,which transfers the style of a prototype motion to the final generated dance.In addition,we present an aesthetically labelled music-dance repertoire(MDR)for both efficient learning of the cross-modality embedding,and understanding of the aesthetic connections between music and motion.We demonstrate that our repertoire-based framework is robustly extensible in both content and style.Both quantitative and qualitative experiments have been carried out to validate the efficiency of our proposed model.展开更多
基金supported by the Theme-based Research Scheme,Research Grants Council of Hong Kong,China(T45-205/21-N).
文摘To generate dance that temporally and aesthetically matches the music is a challenging problem in three aspects.First,the generated motion should be beats-aligned to the local musical features.Second,the global aesthetic style should be matched between motion and music.And third,the generated motion should be diverse and non-self-repeating.To address these challenges,we propose ReChoreoNet,which re-choreographs high-quality dance motion for a given piece of music.A data-driven learning strategy is proposed to efficiently correlate the temporal connections between music and motion in a progressively learned cross-modality embedding space.The beats-aligned content motion will be subsequently used as autoregressive context and control signal to control a normalizing-flow model,which transfers the style of a prototype motion to the final generated dance.In addition,we present an aesthetically labelled music-dance repertoire(MDR)for both efficient learning of the cross-modality embedding,and understanding of the aesthetic connections between music and motion.We demonstrate that our repertoire-based framework is robustly extensible in both content and style.Both quantitative and qualitative experiments have been carried out to validate the efficiency of our proposed model.
