Multi‐Agent Learning With Hierarchical Biomechanical Priors for Efficient 3D Human Pose Estimation in Virtual Reality

ABSTRACT In virtual reality (VR) applications, real‐time and robust 3D human pose estimation is paramount to enhance user experience, yet existing methodologies often encounter challenges such as high computational burden, occlusion sensitivity, and inadequate adaptation to complex actions. To mitigate these issues, we propose a novel 3D human pose estimation method based on a multi‐agent hierarchical biomechanical priors architecture. This method achieves efficient heatmap prediction in the local feature space through a parallel agents architecture, while simultaneously integrating a hierarchical loss function and dynamic context modeling. It incorporates virtual avatars' geometric constraints into network training, thereby enhancing pose plausibility and effectively addressing occlusion and intricate actions. Moreover, it substantially improves cross‐frame stability and estimation accuracy in occlusion scenarios through multiview spatiotemporal consistency optimization. Compared to existing methods, the proposed framework provides adaptability to the unique demands of virtual environments with reduced computational cost. We experimentally validate our approach on the widely used Human3.6M and MPI‐INF‐3DHP datasets, and further demonstrate through ablation experiments that the dynamic occlusion compensation module, which fuses multimodal perception with a spatio‐temporal diffusion mechanism, significantly enhances the robustness of pose estimation under occlusion scenarios with virtual costumes.