Kinematic Diversity and Rhythmic Alignment in Choreographic Quality Transformers for Dance Quality Assessment

In recent years, the dance entertainment industry has experienced significant growth, driven by the desire of consumers to learn and improve their dancing skills. To effectively improve their skills, dancers require evaluation and feedback, which traditionally relies heavily on professional dancers. To address this challenge, researchers have proposed objective assessment methods for dance performance via kinematic data captured by sensors. However, these existing methods primarily focus on assessing the rhythmic accuracy of movements synchronized to music. In this paper, we propose Dance Quality Assessment (DanceQA) Framework to evaluate dance performance, considering choreographic factors that are important criteria in subjective DanceQA. We find that kinematic diversity and rhythmic alignment are significant choreographic factors from human perception perspective. Based on these factors, we design two metrics: kinematic information entropy (KIE) and kinematic-music beat similarity (BSIM). Our study demonstrates that these metrics are closely related to specific body parts in each choreography. To validate the effectiveness of our metrics, we capture dance performance by OptiTrack system providing precise three-dimensional data at very high sampling rate. We then label their dance quality via subjective test. The metrics give strong correlation with subjective opinion, but it is difficult to tell which body part is the most correlated. To comprehensively understand the dance quality, we propose choreographic quality transformers (CQTs), which learn the aforementioned choreographic factors by embedding KIE and BSIM into attention matrices. In numerous experiments, the CQTs outperforms previous methods, graph convolutional networks and multimodal transformers, at least by up to 0.146 in correlation coefficient.