Synthetic Hands Generator for RGB Hand Tracking

In this study, we addressed the challenging problem of 2D hand-pose tracking based on an RGB-only sequence by using a hand data generator. For training various deep networks on hand-pose tracking, we propose a synthetic hand generator based on an application. Our generator could be combined with a kinematic hand model to generalize well to unseen data. In addition, it is robust to occlusions and varying camera viewpoints and leads to anatomically smooth hand motions. Our generator also allows to set the range of each property and add objects (hand models and backgrounds) easily to the application. This greatly diversifies the architecture and improves performance of hand pose tracking. We evaluated our generator by comparing with other public hand datasets and propose a novel annotation technique for accurate 2D (3D) hand labeling and joint angles even in case of partial occlusions. We demonstrate that the dataset generated through our generator outperforms other public datasets with only challenging RGB.