Sequence-to-Sequence Labanotation Generation Based on Motion Capture Data

Abstract

Labanotation is an important notation system for recording dances. Automatically generating Labanotation scores from motion capture data has attracted more interest in recent years. Current methods usually focus on individual movement segments and generate Labanotation symbols one by one. This requires segmenting the captured data sequence in advance. Manual segmentation will consume a lot of time and effort, while automatic segmentation may not be reliable enough. In this paper, we propose a sequence-to-sequence approach that can generate Labanotation scores from unsegmented motion data sequences. First, we extract effective features from motion capture data based on body skeleton analysis. Then, we train a neural network under the encoder-decoder architecture to transform the motion feature sequences to corresponding Labanotation symbols. As such, the dance score is generated. Experiments show that the proposed method performs favorably against state-of-the-art algorithms in the automatic Labanotation generation task.