Neural ordinary differential equation for irregular human motion prediction

human motion prediction often assumes that the input sequence is of fixed frame rates. However, in real-world applications, the motion capture system may work unstably sometimes and miss some frames, which leads to inferior performance. To solve this problem, this paper leverages neural Ordinary Differential Equations and proposes a human Motion Prediction method named MP-ODE to handle irregular-time human pose series. First, a Difference Operator and a Positional Encoding are proposed to explicitly provide the kinematic and time information for the model. Second, we construct the encoder-decoder model with ODE-GRU unit, which enables us to learn continuous-time dynamics of human motion. Third, a Quaternion Loss transforms exponential maps to quaternion to train MP-ODE. The Quaternion Loss can avoid the discontinuities and singularities of exponential maps, boosting the convergence of the model. Comprehensive experiments on Human3.6 m and CMU-Mocap datasets demonstrate that the proposed MP-ODE achieves promising performance in both normal and irregular-time conditions.