Hand Pose Estimation

Hand pose estimation aims to accurately determine the 3D position and orientation of a hand's joints from images or video, enabling applications in human-computer interaction, robotics, and healthcare. Current research emphasizes improving robustness to challenging conditions like occlusion and varying lighting, often employing transformer networks, diffusion models, and graph convolutional networks to capture spatial and temporal relationships between hand joints. These advancements are driving progress in areas such as sign language recognition, gesture-based control of devices, and automated analysis of human activities, particularly in egocentric settings.