3D Human Motion Prediction: A Survey
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by
Kedi Lyu, Haipeng Chen, Zhenguang Liu, Beiqi Zhang, Ruili Wang
2022
Abstract
3D human motion prediction, predicting future poses from a given sequence, is
an issue of great significance and challenge in computer vision and machine
intelligence, which can help machines in understanding human behaviors. Due to
the increasing development and understanding of Deep Neural Networks (DNNs) and
the availability of large-scale human motion datasets, the human motion
prediction has been remarkably advanced with a surge of interest among academia
and industrial community. In this context, a comprehensive survey on 3D human
motion prediction is conducted for the purpose of retrospecting and analyzing
relevant works from existing released literature. In addition, a pertinent
taxonomy is constructed to categorize these existing approaches for 3D human
motion prediction. In this survey, relevant methods are categorized into three
categories: human pose representation, network structure design, and
prediction target. We systematically review all relevant journal and
conference papers in the field of human motion prediction since 2015, which are
presented in detail based on proposed categorizations in this survey.
Furthermore, the outline for the public benchmark datasets, evaluation
criteria, and performance comparisons are respectively presented in this paper.
The limitations of the state-of-the-art methods are discussed as well, hoping
for paving the way for future explorations.
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