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

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.

Keywords

• Human motion prediction
• deep learning
• neural network
• equivariant models