End-to-End Method For Labanotation Generation From Continuous Motion Capture Data

Labanotation is a standardized notation system for human motion recording and archiving. Existing methods for automatic Labanotation generation require pre-segmentation of continuous motion and only recognize single movement each time. The poor performance of pre-segmentation will subsequently lead to inaccurate movement recognition and Labanotation generation. In this paper, we proposed an end-to-end trainable method for recognizing continuous motion and generating corresponding Laban symbols without presegmentation. Firstly, we design Lie group feature to represent rotation information of joints and bones underlying in continuous motion capture data. Secondly, we employ Convolutional Recurrent Neural Network (CRNN) to jointly analyze motion sequences in spatial and temporal domain. Furthermore, we adopt Connectionist Temporal Classification (CTC) layer to translate per-frame predictions into Laban symbol sequence, which enables analysis on motion sequences with arbitrary lengths. Experiments on continuous motion capture dataset demonstrate the effectiveness of proposed method and its superiority compared with the state-of-the-art.