Towards Realistic Human Motion Prediction with Latent Diffusion and Physics-Based Models

Many applications benefit from the prediction of 3D human motion based on past observations, e.g., human–computer interactions, autonomous driving. However, while existing methods based on encoding–decoding achieve good performance, prediction in the range of seconds still suffers from errors and motion switching scarcity. In this paper, we propose a Latent Diffusion and Physical Principles Model (LDPM) to achieve accurate human motion prediction. Our framework performs human motion prediction by learning information about the potential space, noise-generated motion, and combining physical control of body motion, where physics principles estimate the next frame through the Euler–Lagrange equation. The framework effectively accomplishes motion switching and reduces the error accumulated over time. The proposed architecture is evaluated on three challenging datasets: Human3.6M (Human 3D Motion Capture Dataset), HumanEva-I (Human Evaluation dataset I), and AMASS (Archive of Motion Capture as Surface Shapes). We experimentally demonstrate the significant superiority of the proposed framework in the prediction range of seconds.