ChoreoNet: Towards Music to Dance Synthesis with Choreographic Action Unit

Abstract

Dance and music are two highly correlated artistic forms. Synthesizing dance motions has attracted much attention recently. Most previous works conduct music-to-dance synthesis via directly music to human skeleton keypoints mapping. Meanwhile, human choreographers design dance motions from music in a two-stage manner: they firstly devise multiple choreographic dance units (CAUs), each with a series of dance motions, and then arrange the CAU sequence according to the rhythm, melody and emotion of the music. Inspired by these, we systematically study such two-stage choreography approach and construct a dataset to incorporate such choreography knowledge. Based on the constructed dataset, we design a two-stage music-to-dance synthesis framework ChoreoNet to imitate human choreography procedure. Our framework firstly devises a CAU prediction model to learn the mapping relationship between music and CAU sequences. Afterwards, we devise a spatial-temporal inpainting model to convert the CAU sequence into continuous dance motions. Experimental results demonstrate that the proposed ChoreoNet outperforms baseline methods (0.622 in terms of CAU BLEU score and 1.59 in terms of user study score).

10 pages, 5 figures, Accepted by ACM MM 2020

Cited by in corpus (7)

• Listen, Denoise, Action! Audio-Driven Motion Synthesis with Diffusion Models
• Transflower: probabilistic autoregressive dance generation with multimodal attention
• Rhythm is a Dancer: Music-Driven Motion Synthesis with Global Structure
• DanceGen: Supporting Choreography Ideation and Prototyping with Generative AI
• Dual Learning Music Composition and Dance Choreography
• Learning Music-Dance Representations through Explicit-Implicit Rhythm Synchronization
• DanceCamAnimator: Keyframe-Based Controllable 3D Dance Camera Synthesis