基于先验补偿的时间持续学习用于人体运动预测
Temporal Continual Learning with Prior Compensation for Human Motion Prediction
AI总结：
本文提出时间持续学习框架TCL和先验补偿因子PCF，通过多阶段训练补偿历史预测先验信息，缓解长短时预测冲突，并在四个HMP基准上验证了其有效性与即插即用灵活性。
摘要：
人体运动预测（Human Motion Prediction, HMP）旨在根据过去的运动序列预测未来不同时刻的姿态。以往的方法将不同时刻的预测同等对待，导致两个主要局限：短期预测的学习受到对长期预测关注的阻碍，以及将过去预测中的先验信息纳入后续预测的能力有限。在本文中，我们提出了一种新颖的多阶段训练框架，称为时间持续学习（Temporal Continual Learning, TCL），以应对上述挑战。为了更好地保留先验信息，我们引入了先验补偿因子（Prior Compensation Factor, PCF）。我们将其纳入模型训练，以补偿丢失的先验信息。此外，我们通过理论推导得到了一个更合理的优化目标。需要指出的是，我们的TCL框架可以轻松地与不同的HMP骨干模型集成，并适应各种数据集和应用。在四个HMP基准数据集上的大量实验证明了TCL的有效性和灵活性。代码可在 https://github.com/hyqlat/TCL 获取。
英文摘要：
Human Motion Prediction (HMP) aims to predict future poses at different moments according to past motion sequences. Previous approaches have treated the prediction of various moments equally, resulting in two main limitations: the learning of short-term predictions is hindered by the focus on long-term predictions, and the incorporation of prior information from past predictions into subsequent predictions is limited. In this paper, we introduce a novel multi-stage training framework called Temporal Continual Learning (TCL) to address the above challenges. To better preserve prior information, we introduce the Prior Compensation Factor (PCF). We incorporate it into the model training to compensate for the lost prior information. Furthermore, we derive a more reasonable optimization objective through theoretical derivation. It is important to note that our TCL framework can be easily integrated with different HMP backbone models and adapted to various datasets and applications. Extensive experiments on four HMP benchmark datasets demonstrate the effectiveness and flexibility of TCL. The code is available at https://github.com/hyqlat/TCL.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
