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.

翻译：人体运动预测旨在根据过去的人体运动轨迹预测未来的姿态序列。为解决该问题，本文提出FreqMRN——一种综合考虑人体运动学结构与运动时间平滑性的人体运动预测框架。具体而言，FreqMRN首先通过运动注意力模块生成固定大小的运动历史摘要，从而避免因过长运动输入导致的预测偏差。随后，在所提出的时空感知损失、速度感知损失及全局平滑感知损失的监督下，FreqMRN通过运动精化模块在姿态空间与频率空间之间反复转换运动表征，从而迭代优化预测结果。我们在Human3.6M、AMASS及3DPW等多个标准基准数据集上对FreqMRN进行评估。实验结果表明，FreqMRN在短期与长期预测任务中均显著优于现有方法，并展现出更优的鲁棒性。