컴퓨터 그래픽스 학회인 SIGGRAPH Asia 2019에 Motion AI 팀의 Human Motion Denoising Using Attention-Based Bidirectional Recurrent Neural Network 연구가 소개되었습니다.
Abstract
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.
Authors
Seonguk Kim, Hanyoung Jang(NCSOFT), Jongmin Kim
Proceeding
SIGGRAPH Asia ‘19 ACM SIGGRAPH 2019 Poster Article