Skeleton-Aware Neural Sign Language Translation

As an essential communication way for deaf-mutes, sign languages are expressed by human actions. To distinguish human actions for sign language understanding, the skeleton which contains position information of human pose can provide an important cue, since different actions usually correspond to different poses/skeletons. However, skeleton has not been fully studied for Sign Language Translation (SLT), especially for end-to-end SLT. Therefore, in this paper, we propose a novel end-to-end Skeleton-Aware neural Network (SANet) for video-based SLT. Specifically, to achieve end-to-end SLT, we design a self-contained branch for skeleton extraction. To efficiently guide the feature extraction from video with skeletons, we concatenate the skeleton channel and RGB channels of each frame for feature extraction. To distinguish the importance of clips, we construct a skeleton-based Graph Convolutional Network (GCN) for feature scaling, i.e., giving importance weight for each clip. The scaled features of each clip are then sent to a decoder module to generate spoken language. In our SANet, a joint training strategy is designed to optimize skeleton extraction and sign language translation jointly. Experimental results on two large scale SLT datasets demonstrate the effectiveness of our approach, which outperforms the state-of-the-art methods. Our code is available at https://github.com/SignLanguageCode/SANet.