Hand Keypoints

Hand keypoint detection and estimation are crucial for applications ranging from human-computer interaction to sign language recognition and 3D hand modeling. Current research focuses on improving accuracy and robustness across diverse conditions (lighting, viewpoints, occlusions) using deep learning architectures, including multi-task learning, diffusion models, and multi-scale networks with attention mechanisms. These advancements leverage both RGB and thermal imaging data, often incorporating self-supervised learning techniques to reduce reliance on large annotated datasets. The resulting improvements in hand pose estimation and gesture recognition have significant implications for various fields, including healthcare, robotics, and virtual reality.