Multiscale Global Adaptive Attention Graph Neural Network

Abstract

Dynamic multiscale graph neural networks have high motion prediction errors due to the low correlation between the internal joints of body parts and the limited perceptual fields. A multiscale global adaptive attention graph neural network for human motion prediction is proposed to reduce motion prediction errors. Firstly, a multi-distance partitioning strategy for dividing skeleton joint is proposed to improve the degree of temporal and spatial correlation of body joint information. Secondly, a global adaptive attention spatial temporal graph convolutional network is designed to dynamically enhance the network??s attention to the spatial temporal joints contributing to a motion in combination with global adaptive attention. Finally, this paper integrates the above two improvements into the graph convolutional neural network gate recurrent unit to enhance the state propagation performance of the decoding network and reduce prediction errors. Experimental results show that the prediction error of the proposed method is decreased on Human 3.6M dataset, CMU Mocap dataset and 3DPW dataset compared with state-of-the-art methods.

Keywords

• motion prediction; multi-distance partitioning strategy; global adaptive attention; spatial temporal graph convolution neural network; gated recurrent unit