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Human Motion Prediction: Assessing Direct and Geometry-Aware Approaches in 3D Space | IEEE Journals & Magazine | IEEE Xplore

Human Motion Prediction: Assessing Direct and Geometry-Aware Approaches in 3D Space


Annual number of publications on human motion prediction from 2017 to September 2023 across various neural network architectures: RNN, CNN, GCN, Generative models, and Tr...

Abstract:

Predicting 3D human motion is a complex task, owing to the unpredictable nature of human movements. The influx of deep learning innovations and the availability of extens...Show More

Abstract:

Predicting 3D human motion is a complex task, owing to the unpredictable nature of human movements. The influx of deep learning innovations and the availability of extensive datasets have intensified research interest in this field. This survey provides an exhaustive review of human motion prediction algorithms and categorizes them according to their core architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Graph Convolutional Networks (GCNs), Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Transformers, and Equivariant Neural Networks (ENNs). Our key contribution is a systematic presentation of the latest prediction methodologies, classified into direct and geometry-aware modeling. We begin with the problem formulation of human motion prediction, explore assorted techniques, and discuss data representation, accompanied by a list of accessible datasets. We also identify and analyze the ongoing challenges and limitations of the current algorithms, offering insights into potential future developments in this domain.
Annual number of publications on human motion prediction from 2017 to September 2023 across various neural network architectures: RNN, CNN, GCN, Generative models, and Tr...
Published in: IEEE Access ( Volume: 12)
Page(s): 104643 - 104662
Date of Publication: 29 July 2024
Electronic ISSN: 2169-3536

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