Towards Accurate Human Motion Prediction via Iterative Refinement
Jiarui Sun Girish Chowdhary
Human motion prediction aims to forecast an upcoming pose sequence given a
past human motion trajectory. To address the problem, in this work we propose
FreqMRN, a human motion prediction framework that takes into account both the
kinematic structure of the human body and the temporal smoothness nature of
motion. Specifically, FreqMRN first generates a fixed-size motion history
summary using a motion attention module, which helps avoid inaccurate motion
predictions due to excessively long motion inputs. Then, supervised by the
proposed spatial-temporal-aware, velocity-aware and global-smoothness-aware
losses, FreqMRN iteratively refines the predicted motion though the proposed
motion refinement module, which converts motion representations back and forth
between pose space and frequency space. We evaluate FreqMRN on several standard
benchmark datasets, including Human3.6M, AMASS and 3DPW. Experimental results
demonstrate that FreqMRN outperforms previous methods by large margins for both
short-term and long-term predictions, while demonstrating superior robustness.