## 1 Introduction  
Forecasting future human motion based on historical observations and current cues has long been a popular research topic in the field of computer vision, bearing significant implications for applications such as autonomous driving, metaverse-based human-computer interactions, and abnormal behavior monitoring in public spaces [1, 2, 3]. Benefiting from advances in 2D and 3D pose estimation algorithms [4, 5], extracting human skeleton data from videos to describe human actions or movements has become increasingly prevalent. As a result, skeleton-based human motion prediction has garnered growing attention. Different from inanimate entities governed solely by physical laws, human motion is often controlled by autonomous neural activities or subconscious processes, presenting significant challenges characterized by nonlinearity and multivariate dynamics. In previous studies, conventional temporal data modeling methods, such as linear dynamical systems [6], hidden Markov models [7], and Gaussian processes [8], have been employed to model human body dynamics. However, these models exhibit limited modeling capabilities due to constraints on the number of parameters and optimization techniques, resulting in unsatisfactory performance. Recently, artificial neural networks have garnered considerable attention from researchers for their superior nonlinear modeling capabilities and have been applied to human motion prediction. Specifically, recurrent neural network (RNN) models have been utilized to establish temporal dependencies between frames [9; 10]. Graph convolutional neural networks (GCNs) effectively model the spatial coupling relationships of joints or body parts [11; 12]. Additionally, self-attention mechanisms enable the parallel capture of contextual dependencies and can be employed to adaptively generate corresponding graph structures based on different behaviors [13]. Scrutinizing these aforementioned methods, we empirically observe that they primarily focus on modeling historical observation sequences, attempting to fully exploit the characteristics of the observed motion, particularly the kinematic features. Specifically, relying solely on the kinematic information of human joints renders the predictions highly sensitive to temporal scales, making them susceptible to prediction failures under low sampling frequencies or during long-term prediction tasks. For example, [PERSON] et al. [14] found that existing methods often struggle with maintaining accuracy over longer prediction horizons due to error accumulation and the complexity of modeling human dynamics. [PERSON] et al. [15] observed that traditional one-step prediction schemes fail to exploit transitional patterns in human motion, leading to degraded accuracy over longer prediction horizons. Additionally, existing joint feature extraction methods are often limited to single-scale or fixed-joint topological relationships, which significantly restricts the network's ability to capture the coupling relationships between joints under different motion patterns [16; 17]. To address the limitations of solely utilizing kinematic information, this study incorporates dynamic information related to joint movements. Specifically, we model the human body as a multi-rigid-body system with multiple rotational joints, employing robotic modeling techniques to describe the dynamic processes of a human skeleton. Joint torques are estimated using the Lagrangian method and integrated as dynamic features. Additionally, we design a KD-fused Graph-GRU (Kinematic and Dynamics Fused Graph Gate Recurrent Unit) that combines both kinematic and dynamic information to predict future motion. Experimental results validate the effectiveness of incorporating dynamic information and demonstrate the superior performance of the proposed prediction model. To address the constraints imposed by single-scale and fixed-joint topological relationships, we develop a multi-scale mixed-graph neural network.