3D Human Motion Prediction: A Survey

TL;DR

This survey comprehensively reviews 3D human motion prediction methods, categorizing approaches, datasets, and evaluation metrics, highlighting recent advances and limitations to guide future research in this evolving field.

Contribution

It provides a systematic taxonomy of existing methods, detailed analysis of literature since 2015, and discusses benchmarks and limitations to advance future work.

Findings

Categorization of methods into pose representation, network design, and prediction target.

Summary of benchmark datasets and evaluation criteria.

Discussion of limitations and future directions in 3D human motion prediction.

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 \textit{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.