KiJacoMMP: Kinematics-Aware Jacobian Iterative Solver for Multi-Person Motion Prediction

Multi-person motion prediction aims to infer the future 3D poses of all individuals in a scene from historical observations. Existing methods typically predict future joint coordinates directly by learning spatiotemporal correlations. However, small coordinate errors do not guarantee that a predicted pose can be continuously reached from the current motion state. To address these issues, we propose KiJacoMMP, a kinematics-aware jacobian iterative solver for multi-person motion prediction. KiJacoMMP firstly exploits acceleration field encoder characterizing the evolution of individual velocities and interaction relation encoder modeling spatial dependencies among different individuals. Subsequently, the model takes the last observed pose as the initial condition, integrates the preliminary velocities frame by frame into joint positions, and constructs constraint residuals from predefined skeletal length. We further derive a sparse skeletal Jacobian matrix and an edge coupling matrix, transforming each nonlinear skeletal correction into a damped linearized system. Through multiple iterations, the system jointly updates shared joints, thereby producing kinematically feasible future trajectories while preserving the preliminary motion trend as much as possible. The proposed method requires neither body mass nor an external physics simulator. Experiments on current multi-person motion prediction datasets demonstrate the effectiveness of our proposed method.