An Effective Conditional Transformer-Based Diffusion Model for Three-Dimensional Human Motion Prediction

Human motion prediction is a classic problem in computer vision and graphics, and the prediction of human motion diversity has a wide range of practical applications. To tackle this problem, this study proposes predicting the future motion diversity of the human body based on conditional denoising diffusion probabilistic models combined with the kinematics of human joints. First, the observed and predicted sequences were integrated into the same sample space using the mask mechanism, and Gaussian noise was gradually injected into the predicted sequence leveraging the cosine noise scheduler to destroy the sequence structure. Subsequently, the spatial-temporal feature extractor and channel enhancement module were used to form a denoiser to learn the temporal dynamic evolution of the sample and the potential correlation between the nodes in the diffusion process to complete the noise prediction and restore the sample information. The proposed method was verified on the Human3.6M and HumanEva-I datasets, and the experimental results show that the proposed method is competitive with previous methods in diversity prediction.