Predicting Human Motion Using Key Subsequences

Human motion prediction is an important task in computer vision, and has a wide range of applications, such as autonomous driving and human-robot interaction. Usually, human motion tends to repeat itself and follows patterns that are well-represented by a few short key subsequences. Based on the above observations, we propose an attention-based feed-forward network, which is explicitly guided by the key subsequences, for human motion prediction. Specifically, we obtain the key subsequences by clustering, extract motion attention by the similarity between the observed poses and the motion context of corresponding key subsequences, and aggregate the relevant key subsequences by a graph convolutional network to predict human motion. Experimental results on public human motion datasets show that our method achieves better performance over state-of-the-art methods in motion prediction.