An Automatic Framework For Generating Labanotation Scores From Continuous Motion Capture Data

Labanotation is a widely used dance notation system. The topic of generating Labanotation scores from captured dance motion data has attracted more research interest in recent years. Current methods usually generate Laban symbols via recognizing motion segments that each contains a single dance movement. However, they rely on the manual segmentation of raw dance motion sequences, which can cost a lot of time and effort. In this paper, we present a fully automatic framework to generate Labanotation scores from continuous motion data. First, we split the captured dance data to motion segments based on the Laban theory of body weight support transferring. Then, based on the segmented data, we utilize a network with both 1D-convolutional and recurrent layers to recognize body movements and generate Laban symbols. As such, the Labanotation score is created. Extensive experiments show that the proposed automatic framework performs favorably against previous solutions.