Pose evaluation for dance learning application using joint position and angular similarity

Lee, Jaejun; Choi, Jong-Hyeok; Chuluunsaikhan, Tserenpurev; Nasridinov, Aziz

doi:10.1145/3410530.3414402

Cited by 7 publications

(2 citation statements)

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“…To allow use by dancers without technical skills or ownership of such specialized hardware, a support system should offer quick access to moments where their dances are not well-synchronized while utilizing commodity video recording devices. Recent work [27] has demonstrated a mobile system to offer feedback on dances performed by a single person. However, the system only considers the differences in poses, and lacks the consideration of the temporal alignments of dancers' movements, which is another critical component that creates visual aesthetics seen in synchronized dancing.…”

Section: Introductionmentioning

confidence: 99%

“…The main advantage of SyncUp is to offer quick access to the portions of recordings where the system considers that dances are not well-synchronized in an in-situ manner. Unlike existing work [1,6,27], SyncUp considers both pose similarity among dancers and the temporal alignment of motions, another key element of synchronized dancing for inferring the degree of synchronization. SyncUp then visualizes them as 1D heatmaps (Figure 1 right).…”

Section: Introductionmentioning

confidence: 99%

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SyncUp: Vision-based Practice Support for Synchronized Dancing

Zhou,

Xu,

Yatani

2021

Preprint

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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.CCS Concepts: • Human-centered computing → Interactive systems and tools.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%