Human Motion Prediction Based on Bidirectional-GRU and Attention Mechanism Model

Aiming at the problem that the first frame of human motion prediction is discontinuous and the accurate prediction time is short due to the influence of uncertain factors such as motion speed and ampli- tude, a sequence to sequence model (BiAGRU-seq2seq) based on bidirectional GRU and attention mecha- nism is proposed. The model encoder section uses a bidirectional GRU, which allows data to be input from two opposite directions at the same time. The decoder section uses the GRU plus attention mechanism structure to encode the encoder output into a vector sequence containing multiple subsets. The input and output of the decoder are then simultaneously sent to the residual architecture to simulate the speed of the human body and bring the predicted value closer to the true value. In the TensorFlow framework, human motion prediction experiments were performed using the public motion capture dataset human3.6m. Ex- perimental results demonstrate that the proposed model can not only greatly reduce the short-term motion prediction error but also accurately predict multiple motion frames.