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Continual Prior Compensation for Human Motion Prediction

This paper introduces Continual Prior Compensation (CPC), a temporal continual learning framework for human motion prediction that trains models progressively across short- and long-term prediction subtasks to avoid interference. It adds a learnable Prior Compensation Factor (PCF) to mitigate prior knowledge forgetting, with a theoretical bound for joint optimization)Skip. An enhanced version, CPC++, uses fine-grained factors per subtask. Experiments on three benchmarks with multiple backbones show improved flexibility and effectiveness.

Why it matters: Provides a novel continual learning method with theoretical grounding and broad backbone compatibility, offering practical gains for HMP.