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.