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In many industrial applications, it is necessary to obtain accurate human motion prediction. The prediction of human movement based on a 3D skeleton, which forecasts future postures using the human skeletal structure, significantly aids in enabling machines to understand human behavior and react more intelligently. Additionally, by predicting human actions, potential dangers can be perceived, especially in safety-related issues, necessitating a broader range of predictions. In this paper, considering the greater diversity generated by Variational Autoencoders (VAEs), we employ VAEs for prediction. In contrast to other studies, our primary focus is on obtaining more diverse samples. Extensive experiments demonstrate that our proposed model performs well on the Human 3.6M [35] and HumanEva-I [36] datasets.

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Diverse Gaussian Sampling for Human Motion Prediction

  • 会议论文

  • 发表日期：

    2025年

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  • Jiefu Luo,
  • Jiansheng Wang,
  • Zhenfei Liu,
  • Jun Cheng

摘要

In many industrial applications, it is necessary to obtain accurate human motion prediction. The prediction of human movement based on a 3D skeleton, which forecasts future postures using the human skeletal structure, significantly aids in enabling machines to understand human behavior and react more intelligently. Additionally, by predicting human actions, potential dangers can be perceived, especially in safety-related issues, necessitating a broader range of predictions. In this paper, considering the greater diversity generated by Variational Autoencoders (VAEs), we employ VAEs for prediction. In contrast to other studies, our primary focus is on obtaining more diverse samples. Extensive experiments demonstrate that our proposed model performs well on the Human 3.6M [35] and HumanEva-I [36] datasets.