| Paper ID | MMSP-3.3 |
| Paper Title |
AN ATTENTION-SEQ2SEQ MODEL BASED ON CRNN ENCODING FOR AUTOMATIC LABANOTATION GENERATION FROM MOTION CAPTURE DATA |
| Authors |
Min Li, Zhenjiang Miao, Beijing Jiaotong University, China; Xiao-Ping Zhang, Ryerson University, Canada; Wanru Xu, Beijing Jiaotong University, China |
| Session | MMSP-3: Multimedia Synthesis and Enhancement |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 14:00 - 14:45 |
| Presentation Time: | Wednesday, 09 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Multimedia Signal Processing: Multimedia Applications |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Labanotation is an important notation system widely used for recording dances. Numerous methods have been proposed for automatic Labanotation generation from motion capture data. Recently, the sequence-to-sequence (seq2seq) model is proposed. However, the encoder of the model only encodes the temporal information of motion data, lacking the encoding for spatial information. And it is challenging for the decoder to align input and output sequences due to the imbalance of the sequence lengths. In this paper, we propose an attention-seq2seq model based on Convolutional Recurrent Neural Network (CRNN). The proposed model employs an encoder based on CRNN to learn the spatial-temporal information of motion data and applies an attention mechanism to align each target Laban symbol with relevant parts of the input motion data in decoding. Experiments show that the proposed method performs favorably against state-of-the-art algorithms in the automatic Labanotation generation task. |
