Human Motion Denoising Using Attention-Based Bidirectional Recurrent Neural Network
Seong Uk Kim, Hanyoung Jang, Jongmin Kim · 2019
In this paper, we propose a novel method of denoising human motion using a bidirectional recurrent neural network (BRNN) with an attention mechanism. The corrupted motion that is captured from a single 3D depth sensor camera is automatically fixed in the well-established smooth motion manifold. Incorporating an attention mechanism into BRNN achieves better optimization results and higher accuracy because a higher weight value is selectively given to the more important input pose at a specific frame for encoding the input motion when compared to other deep learning frameworks. The results show that our approach efficiently handles various types of motion and noise. We also experiment with different features to find the best feature and believe that our method will be sufficiently desirable to be used in motion capture applications as a post-processing step after capturing human motion.