  Nowadays, the need for large amounts of carefully and complexly annotated
data for the training of computer vision modules continues to grow.
Furthermore, although the research community presents state of the art
solutions to many problems, there exist special cases, such as the pose
estimation and tracking of a glove-wearing hand, where the general approaches
tend to be unable to provide an accurate solution or fail completely. In this
work, we are presenting a synthetic dataset1 for 3D pose estimation of
glove-wearing hands, in order to depict the value of data synthesis in computer
vision. The dataset is used to fine-tune a public hand joint detection model,
achieving significant performance in both synthetic and real images of
glove-wearing hands.
