In order to solve the problem that the first frame of human motion prediction is discontinuous, we notice that the prediction time is short due to the influence of uncertain factors such as motion speed and motion amplitude. In this work, an end-to-end model based on bidirectional gating loop unit (GRU) and attention mechanism, biagru-seq2seq, is proposed. The encoder of our deep model adopts bidirectional Gru structure to input data from both positive and negative directions simultaneously. Meanwhile, the decoder part adopts unidirectional GRU structure and adds attention mechanism to encode the output of the encoder into a vector sequence containing multiple subsets. Also, a large-scale graph discovery framework is used to identify the various human action components. Subsequently, the input and output data of the decoder are fed into the residual at the same time. In the designed tensorflow framework, we use human3.6m, the largest open data set of motion capture data at present, to fulfill human motion prediction applications. Comprehensive experimental results have shown that the proposed model can not only substantially reduce the short-term motion prediction error, but also accurately predict multi frame human action recognition.

为了解决人体运动预测的第一帧不连续的问题，我们注意到由于不确定因素（例如运动速度和运动幅度）的影响，预测时间很短。在这项工作中，提出了一种基于双向门控环路单元（GRU）和注意力机制biagru-seq2seq的端到端模型。我们的深度模型的编码器采用双向Gru结构，以同时从正向和负向输入数据。同时，解码器部分采用单向GRU结构，并增加了注意力机制，以将编码器的输出编码为包含多个子集的向量序列。同样，大型图发现框架用于识别各种人类行为组件。随后，解码器的输入和输出数据同时馈入残差。在设计的tensorflow框架中，我们使用human3.6m（目前最大的运动捕捉数据开放数据集）来实现人类运动预测应用。综合实验结果表明，该模型不仅可以大大减少短期运动预测误差，而且可以准确地预测多帧人的动作识别。