Efficient Human Motion Prediction in 3D Using Parallel Enhanced Attention with Sparse Spatiotemporal Modeling

Human motion prediction is critical for applications such as autonomous driving and healthcare. However, existing methods struggle to balance diversity (varied motion patterns), fidelity (realistic and accurate motion), and computational efficiency. Traditional variational autoencoders (VAEs) and generative adversarial networks (GANs) often fail to simultaneously achieve both diversity and fidelity, while diffusion-based approaches suffer from high computational costs. In this paper, we propose a novel sparse spatiotemporal diffusion model that integrates the parallel enhanced attention (PEA) module and spatiotemporal sparse self-attention (STSSA). The PEA module enhances attention to important joints and key motion frames via a dual-branch structure, whereas STSSA reduces redundancy through adaptive sparsification and single-head compression, and exploits spatiotemporal structure to reduce accuracy loss. Experimental results show that the proposed model achieves competitive performance on three benchmark datasets: Human3.6M, HumanEva-I, and AMASS. Compared to the best existing diffusion-based methods, our model significantly enhances efficiency while maintaining similar prediction accuracy. Specifically, the model reduces the number of parameters by 45.7% and shortens the single-sample inference time by 63.0% (from 1.1 s to 0.4 s). Through the joint design of the PEA module and STSSA, the model achieves a balance between efficiency, diversity, and fidelity. The lightweight design and end-to-end framework of the model provide a practical solution for real-time human motion prediction.