A method for stochastic human action prediction based on denoising diffusion probability model
Yulin Wang, Gang Mou, Yuewen Zhao, Tao Song · 2025
This paper proposes a stochastic human action prediction method based on denoising diffusion probability model to address the shortcomings of current motion prediction methods, such as insufficient diversity and deviation of prediction results from reasonable motion intervals. Firstly, a spatio-temporal Transformer denoising diffusion prediction network is constructed to effectively capture the local relationships between 3D joints in each frame, improving the consistency between predicted actions and historical action sequences. Secondly, the graph convolutional network (GCN) and GCN loss are introduced in the discrete cosine transform space to design a prediction action sequence refinement module, which refines the prediction results, improves the accuracy of the prediction results, and further solves problems such as prediction action lag and discontinuity. Finally, the method proposed in this paper was evaluated on the benchmark dataset, and the evaluation results showed that the proposed method was significantly superior to existing prediction methods in terms of accuracy and fidelity.