A CNN model for real time hand pose estimation

Recently convolutional neural networks (CNNs) have been employed to address the problem of hand pose estimation. In this work, we introduce an end-to-end deep architecture that can accurately estimate hand pose through the joint use of model-based and fine-tuning methods. In the model-based stage, we make use of the prior information in hand model geometry to ensure the geometric validity of the estimated poses. Next, we introduce a fine-tuning approach that learns to refine the errors between the model and observed hand. Our approach is validated on three challenging public datasets and achieves state-of-the-art performance.