TLDR: A new framework called Temporal Continual Learning (TCL) improves human motion prediction by using a multi-stage training process. It addresses limitations of previous methods by preventing short-term prediction learning from being hindered by long-term predictions and better incorporating prior information. A Prior Compensation Factor (PCF) is introduced to prevent knowledge forgetting between stages. Experiments show TCL significantly enhances prediction accuracy, especially for long-term movements, and is compatible with various existing models.
Human Motion Prediction (HMP) is a critical area in artificial intelligence, focusing on forecasting future human poses based on observed movements. This technology is vital for applications like autonomous driving, human-robot interaction, and security monitoring, as it helps anticipate and mitigate potential risks.
Traditional HMP methods often treat short-term and long-term predictions equally. This approach has two main drawbacks: learning short-term predictions can be hampered by the complexity of long-term predictions, and the integration of prior information from earlier predictions into later ones is limited. Long-term motion prediction is particularly challenging because future movements can vary significantly, leading to increased uncertainty.
To address these challenges, researchers have introduced a new multi-stage training framework called Temporal Continual Learning (TCL). This innovative approach aims to improve the accuracy of human motion prediction by breaking down the prediction process into several stages. By doing so, TCL allows the model to progressively learn to predict increasing numbers of frames over multiple training stages, leveraging knowledge gained from earlier, shorter-term predictions to inform subsequent, longer-term ones.
A key component of the TCL framework is the Prior Compensation Factor (PCF). This factor is designed to tackle the problem of “knowledge forgetting,” which can occur when the optimization objective changes between training stages. As the model moves from predicting short-term to long-term movements, the prior information learned in earlier stages might fade away. The PCF acts as a learnable variable that compensates for this lost prior information, ensuring that valuable knowledge is preserved throughout the training process. The framework also includes a theoretically derived, more reasonable optimization objective that is easier to optimize.
The flexibility of the TCL framework is a significant advantage; it can be easily integrated with various existing HMP models and adapted to different datasets. Extensive experiments were conducted on four major HMP benchmark datasets, including Human3.6M, CMU-MoCap, 3DPW, and AMASS. The results consistently demonstrated the effectiveness and adaptability of TCL. For instance, when applied to the state-of-the-art PGBIG model, TCL showed notable performance improvements, especially in long-term predictions. This suggests that the prior information from short-term predictions is particularly crucial for more challenging long-term tasks.
Visualizations of predicted motions further illustrate TCL’s ability to generate more accurate results, capturing subtle changes in posture that traditional methods might miss. Additionally, studies on the Prior Compensation Factor showed that its value increases with each training stage, confirming its role in mitigating the loss of prior knowledge. The research also explored the optimal number of tasks and compared TCL with other training strategies, highlighting the benefits of its multi-stage approach and the importance of the PCF.
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While the proposed training framework might slightly increase training time, the testing time remains unchanged. This work holds significant value not only for human motion prediction but also for broader prediction tasks and various backbone models, with potential benefits in fields such as security monitoring, robotics, and autonomous driving. You can find more details in the full research paper available at arXiv:2507.04060.
