HiChor: Hierarchical Choreography Generation from Pop Music with Choreographic Primitives

Choreography is a structured creative process grounded inmusical form, rather than a mere sequence of improvised movements.Professional choreographers design choreography following a set of chore-ographic primitives: (1) organizing movements at the phrase level, (2)refining movements by aligning them with musical beats and energy,and (3) stylizing phrases through semantic text descriptions. However,existing dance generation methods overlook these primitives, leadingto dances that feel structurally incomplete and resemble improvisationrather than professional choreography. Here, we present HiChor: Hi-erarchical Choreography generation from pop music, a framework in-spired by the structured workflow of professional choreographers, whichis grounded in choreographic primitives. HiChor hierarchically generateschoreography from pop music by (1) generating phrase-level choreogra-phy aligned with beats, (2) enhancing choreography by aligning withmusic’s energy, and (3) concatenating all phrase-level choreography andenhancing it through plausibility enhancement to ensure smooth transi-tions between phrases. Furthermore, we introduce a semantic text styl-ization module that converts semantic text descriptions into low-levelchoreography feature captions using an LLM, enabling semantic control.To assess how well the generated choreography adheres to choreographicprinciples, we introduce new metrics—Phrase Diversity, Feature Align-ment, and Text Stylization Score. Experiments demonstrate that HiChoroutperforms existing methods in both choreographic quality and seman-tic stylization.