Recent Advances in Deterministic Human Motion Prediction: A Review
Tenghao Deng Yan Sun
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.