Abstract
The aim of human motion modeling is to understand human behaviors and create reasonable human motion like real people given different priors. With the development of deep learning, researchers tend to leverage data-driven methods to improve the performance of traditional motion modeling methods. In this paper, we present a comprehensive survey of recent human motion modeling researches. We discuss three categories of human motion modeling researches: human motion prediction, humanoid motion control and cross-modal motion synthesis and provide a detailed review over existing methods. Finally, we further discuss the remaining challenges in human motion modeling.
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