Group Spatial Attention for 3D Human Pose Estimation

This paper introduces a novel Group Spatial Attention Module (GSAM) for enhancing 3D Human Pose Estimation (3DHPE) accuracy in complex scenes. Traditional 3DHPE approaches often struggle with occlusions and varied human poses, leading to decreased precision. GSAM addresses these challenges by leveraging group spatial attention mechanisms that dynamically focus on relevant spatial features and interactions among multiple figures within a scene. Our method incorporates a deep learning architecture that integrates GSAM with a stateof-the-art 3DHPE framework, facilitating the extraction of rich, contextual spatial information. We evaluate our approach on standard benchmarks, including Human3.6M and MPI-INF3DHP, demonstrating significant improvements over existing methods in terms of accuracy and robustness against occlusions and pose variations. GSAM sets a new standard for 3DHPE, offering substantial advancements for applications in augmented reality, surveillance, and interactive systems.