Accurate human motion prediction is crucial for safe human-robot collaboration but remains challenging due to the complexity of modeling intricate and variable human movements. This paper presents Parallel Multi-scale Incremental Prediction (PMS), a novel framework that explicitly models incremental motion across multiple spatio-temporal scales to capture subtle joint evolutions and global trajectory shifts. PMS encodes these multi-scale increments using parallel sequence branches, enabling iterative refinement of predictions. A multi-stage training procedure with a full-timeline loss integrates temporal context. Extensive experiments on four datasets demonstrate substantial improvements in continuity, biomechanical consistency, and long-term forecast stability by modeling inter-frame increments. PMS achieves state-of-the-art performance, increasing prediction accuracy by 16.3%-64.2% over previous methods. The proposed multi-scale incremental approach provides a powerful technique for advancing human motion prediction capabilities critical for seamless human-robot interaction.

翻译：准确的人体运动预测对于安全的人机协作至关重要，但由于建模复杂多变人体运动的困难，该任务仍具挑战性。本文提出并行多尺度增量预测（PMS）框架，该新颖框架显式地对多个时空尺度上的增量运动进行建模，以捕捉细微的关节演变和全局轨迹变化。PMS通过并行序列分支编码这些多尺度增量，实现预测的迭代优化。采用包含全时间线损失的多阶段训练程序以整合时序上下文。在四个数据集上的大量实验表明，通过对帧间增量进行建模，在连续性、生物力学一致性和长期预测稳定性方面均取得显著提升。PMS实现了最先进的性能，较先前方法将预测精度提高了16.3%-64.2%。所提出的多尺度增量方法为提升人体运动预测能力提供了一项强大技术，这对实现无缝人机交互至关重要。