Abstract: In order to improve the precision and efficiency of estimating the pose and shape of the 3D human hand from a monocular RGB image and accelerate 3D reconstruction, combining the efficiency of deep neural networks and accuracy of traditional optimization, DeepMANO is proposed. First, a DNN model is used to extract sparse features from image, then using these features to regress MANO model parameters. The regressed parameters are then used to initialize the iterative optimization routine which fits MANO to 3D joints. Finally, the parameters obtained by the routine are employed to supervise the network. Two large hand motion datasets, FreiHand and ObMan, are utilized to train the network. The approach assumes a weak perspective model. Experimental results show that the hand pose error is reduced by 52% and mesh error is reduced by 59% compared with state-of-the-art (SOTA) methods. The running time is about three times faster than SOTA methods.