A survey on deep 3D human pose estimation

3D Human Pose Estimation (3D-HPE) is a highly active and evolving research area in computer vision with numerous applications such as extended reality, action recognition, and video surveillance. The field has significantly advanced with deep learning, public datasets, and enhanced computational power, addressing challenges like depth ambiguity, occlusion, and data scarcity. Researchers confront scenario-specific issues such as ill-posed problems in monocular setups, cross-view aggregation with camera synchronizations in multi-view systems, and inter-person occlusion in multi-person scenarios. This survey comprehensively reviews contemporary strategies covering a technological spectrum including Convolutional Neural Networks, Graph Convolutional Networks, Transformers, and their combinations employed to address these challenges. It includes scenarios such as monocular and multi-view setups, single and multi-person cases, as well as image and video inputs. The survey explores various solution paradigms, including single-stage vs 2D-to-3D lifting, absolute vs relative keypoints, pixel vs voxel vs Neural Radiance Field spaces, and deterministic, probabilistic, or diffusion-based strategies, along with top-down vs bottom-up approaches. It examines advanced learning techniques beyond supervised methods and data augmentation for diverse pose datasets. It analyzes the performance of recent methods on benchmark datasets for different scenarios. Challenges are categorized into common and scenario-specific issues, and future research directions are proposed to foster further advancements in the field. Additionally, key sections are summarized in tables or visual formats for quick understanding. This survey is a valuable resource and a solid reference for researchers in the dynamic landscape of 3D human pose estimation.