The sixth edition of this ECCV2022 workshop aims at gathering researchers who work on 2D/3D hand detection, segmentation, pose estimation, and tracking problems and its applications. This edition will emphasize reduced ground truth labels and focus on topics such as semi-supervised or self-supervised learning for training hand pose estimation systems. Development of RGB-D sensors and camera miniaturization (wearable cameras, smart phones, ubiquitous computing) have opened the door to a whole new range of technologies and applications which require detecting hands and recognizing hand poses in a variety of scenarios, including AR/VR, assisted car driving, robot grasping, and health care. However, labelling accurate real-world hand poses is still non-trivial. Most existing hand pose methods fail to generalize well to the real world scenarios, especially when considering hand-object or hand-hand interaction scenarios. As new multiview video benchmarks have been proposed for the hand-object or hand-hand interaction, our goal is to encourage semi-/self-supervised learning for hand poses to utilize spatial-temporal information and reduce reliance on annotations. We will also cover up a “breadth of application” including sign language recognition, desktop interaction, egocentric views, object manipulations, far range and over-the-shoulder driver footage. The relevant topics include: