  Human motion prediction aims to forecast future human poses given a
historical motion. Whether based on recurrent or feed-forward neural networks,
existing learning based methods fail to model the observation that human motion
tends to repeat itself, even for complex sports actions and cooking activities.
Here, we introduce an attention based feed-forward network that explicitly
leverages this observation. In particular, instead of modeling frame-wise
attention via pose similarity, we propose to extract motion attention to
capture the similarity between the current motion context and the historical
motion sub-sequences. In this context, we study the use of different types of
attention, computed at joint, body part, and full pose levels. Aggregating the
relevant past motions and processing the result with a graph convolutional
network allows us to effectively exploit motion patterns from the long-term
history to predict the future poses. Our experiments on Human3.6M, AMASS and
3DPW validate the benefits of our approach for both periodical and
non-periodical actions. Thanks to our attention model, it yields
state-of-the-art results on all three datasets. Our code is available at
https://github.com/wei-mao-2019/HisRepItself.
