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Human motion prediction aims at generating future frames of human motionbased on an observed sequence of skeletons. Recent methods employ the latesthidden states of a recurrent neural network (RNN) to encode the historicalskeletons, which can only address short-term prediction. In this work, wepropose a motion context modeling by summarizing the historical human motionwith respect to the current prediction. A modified highway unit (MHU) isproposed for efficiently eliminating motionless joints and estimating next posegiven the motion context. Furthermore, we enhance the motion dynamic byminimizing the gram matrix loss for long-term motion prediction. Experimentalresults show that the proposed model can promisingly forecast the human futuremovements, which yields superior performances over related state-of-the-artapproaches. Moreover, specifying the motion context with the activity labelsenables our model to perform human motion transfer.

Long-Term Human Motion Prediction by Modeling Motion Context and Enhancing Motion Dynamic