SyncUp: Vision-based Practice Support for Synchronized Dancing
This addresses the need for better practice tools for synchronized dancers, though it is incremental as it builds on existing vision-based methods for dance analysis.
The paper tackles the problem of inefficient practice for synchronized dancers by developing SyncUp, a system that analyzes videos to quantify pose similarity and temporal alignment, highlighting body parts and segments needing improvement, with evaluations showing good correlation with human ratings.
The beauty of synchronized dancing lies in the synchronization of body movements among multiple dancers. While dancers utilize camera recordings for their practice, standard video interfaces do not efficiently support their activities of identifying segments where they are not well synchronized. This thus fails to close a tight loop of an iterative practice process (i.e., capturing a practice, reviewing the video, and practicing again). We present SyncUp, a system that provides multiple interactive visualizations to support the practice of synchronized dancing and liberate users from manual inspection of recorded practice videos. By analyzing videos uploaded by users, SyncUp quantifies two aspects of synchronization in dancing: pose similarity among multiple dancers and temporal alignment of their movements. The system then highlights which body parts and which portions of the dance routine require further practice to achieve better synchronization. The results of our system evaluations show that our pose similarity estimation and temporal alignment predictions were correlated well with human ratings. Participants in our qualitative user evaluation expressed the benefits and its potential use of SyncUp, confirming that it would enable quick iterative practice.