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<p>Human motion prediction is a significant challenge with broad applications in fields such as robotics, human-computer interaction, and healthcare. Despite the progress achieved by recent deep learning approaches, existing methods often struggle to effectively capture the complex spatial relationships and long-term temporal dependencies inherent in human motion. To address the issue, we propose the Progressive Deeper Attention Network (PDANet), which incorporates multiple GCN-Attention modules of varying depths. This architecture enables the model to extract more comprehensive information from sequential data. Additionally, we enhance the model’s performance through two key improvements: (1) the introduction of joint-relative velocity and temporally perturbed features to distinguish complex motion semantics between dynamic and static joints; and (2) the design of a Multi-Dimensional Joint Fusion (MDJF) module, which employs the Gumbel Softmax method to dynamically learn the optimal fusion strategy for multi-semantic sequences. Extensive experiments demonstrate the effectiveness of our model. The proposed approach outperforms state-of-the-art methods by 2.8%, 4.7%, and 18.8% in terms of MPJPE for human motion prediction on the Human3.6M, AMASS, and 3DPW datasets, respectively.</p>

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Progressively deeper attention networks for 3D human motion prediction

  • 期刊论文

  • 发表日期：

    2025年8月21日

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  • Jiangtao Huang,
  • Dong He,
  • Wenming Cao,
  • Jianqi Zhong

摘要

Human motion prediction is a significant challenge with broad applications in fields such as robotics, human-computer interaction, and healthcare. Despite the progress achieved by recent deep learning approaches, existing methods often struggle to effectively capture the complex spatial relationships and long-term temporal dependencies inherent in human motion. To address the issue, we propose the Progressive Deeper Attention Network (PDANet), which incorporates multiple GCN-Attention modules of varying depths. This architecture enables the model to extract more comprehensive information from sequential data. Additionally, we enhance the model’s performance through two key improvements: (1) the introduction of joint-relative velocity and temporally perturbed features to distinguish complex motion semantics between dynamic and static joints; and (2) the design of a Multi-Dimensional Joint Fusion (MDJF) module, which employs the Gumbel Softmax method to dynamically learn the optimal fusion strategy for multi-semantic sequences. Extensive experiments demonstrate the effectiveness of our model. The proposed approach outperforms state-of-the-art methods by 2.8%, 4.7%, and 18.8% in terms of MPJPE for human motion prediction on the Human3.6M, AMASS, and 3DPW datasets, respectively.