SyncUp: Vision-based Practice Support for Synchronized Dancing

TL;DR

SyncUp is a system that uses vision-based analysis to help dancers improve synchronization by providing visual feedback on pose similarity and timing, streamlining practice and reducing manual video review.

Contribution

It introduces a novel system that automatically analyzes dance videos to quantify synchronization, aiding dancers in iterative practice without manual inspection.

Findings

Pose similarity estimation correlates well with human ratings.

Temporal alignment predictions match human assessments.

Participants found SyncUp beneficial for quick iterative practice.

Abstract

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.