Long-Term Human Motion Prediction by Modeling Motion Context and Enhancing Motion Dynamic
This work addresses the challenge of predicting human movements over extended periods for applications like animation or robotics, representing an incremental improvement.
The paper tackles the problem of long-term human motion prediction by modeling motion context and enhancing motion dynamics, achieving superior performance over state-of-the-art methods.
Human motion prediction aims at generating future frames of human motion based on an observed sequence of skeletons. Recent methods employ the latest hidden states of a recurrent neural network (RNN) to encode the historical skeletons, which can only address short-term prediction. In this work, we propose a motion context modeling by summarizing the historical human motion with respect to the current prediction. A modified highway unit (MHU) is proposed for efficiently eliminating motionless joints and estimating next pose given the motion context. Furthermore, we enhance the motion dynamic by minimizing the gram matrix loss for long-term motion prediction. Experimental results show that the proposed model can promisingly forecast the human future movements, which yields superior performances over related state-of-the-art approaches. Moreover, specifying the motion context with the activity labels enables our model to perform human motion transfer.