Hand Pose

Hand pose estimation, aiming to accurately determine the 3D position and orientation of a hand's joints from images or videos, is a crucial area of computer vision research. Current efforts focus on improving robustness to challenges like occlusion, blur, and varying lighting conditions, often employing transformer networks, diffusion models, and contrastive learning techniques to achieve this. These advancements are driving progress in applications such as human-computer interaction, augmented reality, and action recognition, where precise hand pose understanding is essential for accurate interpretation of human behavior and intent. The development of large-scale, high-quality datasets is also a key focus, enabling the training of more accurate and generalizable models.