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Skeleton-based motion prediction: A survey.

Muhammad Usman1, Jianqi Zhong1

  • 1College of Electronics and Information Communication Engineering, Shenzhen University, Shenzhen, China.

Frontiers in Computational Neuroscience
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Summary
This summary is machine-generated.

This survey explores human motion prediction using 3D skeleton data and deep learning. It reviews recent advancements and discusses future directions in this computer vision research area.

Keywords:
3D human pose representationdeep learninghuman motion predictionskeleton-based motion predictionsurvey

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Area of Science:

  • Computer Vision
  • Multimedia Analysis
  • Artificial Intelligence

Background:

  • Human motion prediction is crucial in computer vision and multimedia analysis.
  • 3D skeleton data offers robust human motion representation, overcoming limitations of RGB data.
  • Early methods relied on RGB data, but recent research integrates skeleton data with deep learning.

Purpose of the Study:

  • To provide a comprehensive overview of human motion prediction using 3D skeleton data.
  • To summarize recent deep learning-based techniques in the field.
  • To review existing literature and discuss future research avenues.

Main Methods:

  • Literature review of recent deep learning approaches for human motion prediction.
  • Analysis of techniques fusing 3D skeleton data with deep learning models.
  • Discussion of the significance and background of human motion prediction research.

Main Results:

  • Deep learning methods combined with 3D skeleton data show promising results for human motion prediction.
  • 3D skeleton data provides a resilient representation for motion analysis.
  • The survey consolidates recent advancements in the field.

Conclusions:

  • Deep learning and 3D skeleton data fusion is a key trend in human motion prediction.
  • Further research is needed to explore advanced deep learning architectures and applications.
  • This survey serves as a valuable resource for researchers in the field.