Human Pose Estimation in 2D and 3D: A Survey of Analytical Methods, Benchmarking Frameworks, and Engineering Applications

This survey presents a comprehensive review of Human Pose Estimation spanning 2D and 3D settings, unifying prior work through a taxonomy of body representations (2D keypoints, 3D skeletons, dense meshes), processing flows (top-down vs. bottom-up), problem formulations (regression vs. detection/heatmaps), and modern learning architectures (CNNs, Transformers, GCNs). We compare reported benchmark results of representative methods across widely used datasets (e.g., COCO, MPII, Human3.6M, 3DPW) and evaluation metrics (AP/OKS, PCK/AUC, MPJPE/PA-MPJPE, PVE), highlighting trade-offs between accuracy, robustness, and efficiency. Despite substantial progress driven by deep learning and temporal modeling, we identify persistent challenges, including costly and biased annotations, domain shift, occlusion, depth ambiguity, multi-person association, and real-time constraints on edge devices. We synthesize emerging directions that target these gaps, data-centric learning, stronger temporal and kinematic priors, and whole-body modeling, and outline deployment-oriented frontiers including generative motion priors, model compression, and on-device inference, framing their implications for engineering systems that demand reliable, low-latency human motion analysis.