In recent years, with the continuous advancement of deep learning and the
emergence of large-scale human motion datasets, human motion prediction
technology has gradually gained prominence in various fields such as
human-computer interaction, autonomous driving, sports analysis, and personnel
tracking. This article introduces common model architectures in this domain
along with their respective advantages and disadvantages. It also
systematically summarizes recent research innovations, focusing on in-depth
discussions of relevant papers in these areas, thereby highlighting
forward-looking insights into the field's development. Furthermore, this paper
provides a comprehensive overview of existing methods, commonly used datasets,
and evaluation metrics in this field. Finally, it discusses some of the current
limitations in the field and proposes potential future research directions to
address these challenges and promote further advancements in human motion
prediction.

本研究论文介绍了人体动作预测技术在人机交互、自动驾驶、运动分析和人员跟踪等各个领域的重要性，并深入讨论了该领域的常见模型架构的优势和劣势，总结了最近的研究创新，并提供了现有方法、常用数据集和评估指标的综合概述。此外，还讨论了该领域当前的一些限制，并提出了未来研究方向以克服这些挑战，推动人体动作预测的进一步发展。