Human motion prediction (HMP) has emerged as a popular research topic due to its diverse applications, but it remains a challenging task due to the stochastic and aperiodic nature of future poses. Traditional methods rely on hand-crafted features and machine learning techniques, which often struggle to model the complex dynamics of human motion. Recent deep learning-based methods have achieved success by learning spatio-temporal representations of motion, but these models often overlook the reliability of motion data. Additionally, the temporal and spatial dependencies of skeleton nodes are distinct. The temporal relationship captures motion information over time, while the spatial relationship describes body structure and the relationships between different nodes. In this paper, we propose a novel spatio-temporal branching network using incremental information for HMP, which decouples the learning of temporal-domain and spatial-domain features, extracts more motion information, and achieves complementary cross-domain knowledge learning through knowledge distillation. Our approach effectively reduces noise interference and provides more expressive information for characterizing motion by separately extracting temporal and spatial features. We evaluate our approach on standard HMP benchmarks and outperform state-of-the-art methods in terms of prediction accuracy.

翻译：人体运动预测（HMP）因其广泛的应用而成为热门研究课题，但由于未来姿态的随机性和非周期性，该任务仍具有挑战性。传统方法依赖于手工特征和机器学习技术，往往难以建模人体运动的复杂动态特性。近年来，基于深度学习的方法通过学习运动的时空表征取得了成功，但这些模型常常忽视运动数据的可靠性。此外，骨骼节点的时序依赖与空间依赖具有本质区别：时序关系捕捉运动随时间的演化信息，而空间关系描述身体结构及不同节点间的关联。本文提出一种利用增量信息的时空分支网络用于HMP，该网络解耦了时域与空域特征的学习过程，可提取更丰富的运动信息，并通过知识蒸馏实现跨域的互补知识学习。通过分离提取时空特征，本方法有效降低了噪声干扰，为运动表征提供了更具表达力的信息。我们在标准HMP基准数据集上评估了该方法，在预测精度上超越了现有最优方法。