3D skeleton-based motion prediction and activity recognition are two interwoven tasks in human behaviour analysis. In this work, we propose a motion context modeling methodology that provides a new way to combine the advantages of both graph convolutional neural networks and recurrent neural networks for joint human motion prediction and activity recognition. Our approach is based on using an LSTM encoder-decoder and a non-local feature extraction attention mechanism to model the spatial correlation of human skeleton data and temporal correlation among motion frames. The proposed network can easily include two output branches, one for Activity Recognition and one for Future Motion Prediction, which can be jointly trained for enhanced performance. Experimental results on Human 3.6M, CMU Mocap and NTU RGB-D datasets show that our proposed approach provides the best prediction capability among baseline LSTM-based methods, while achieving comparable performance to other state-of-the-art methods.

中文翻译：

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用于联合活动识别和运动预测的非局部图卷积网络

基于 3D 骨架的运动预测和活动识别是人类行为分析中的两个相互交织的任务。在这项工作中，我们提出了一种运动上下文建模方法，该方法提供了一种新方法来结合图卷积神经网络和循环神经网络的优点，用于联合人体运动预测和活动识别。我们的方法基于使用 LSTM 编码器-解码器和非局部特征提取注意机制来模拟人体骨骼数据的空间相关性和运动帧之间的时间相关性。提议的网络可以很容易地包括两个输出分支，一个用于活动识别，另一个用于未来运动预测，可以联合训练以提高性能。在Human 3.6M上的实验结果，