MC-hands-1M: A glove-wearing hand dataset for pose estimation
This addresses a specific challenge in computer vision for applications like robotics or VR where gloves are worn, but it is incremental as it builds on existing datasets and models.
The authors tackled the problem of 3D pose estimation for glove-wearing hands by creating a synthetic dataset, which was used to fine-tune a public model, achieving significant performance improvements in both synthetic and real images.
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.