To improve the accuracy of a sheet metal forming simulation, the constitutive model is calibrated using results from multiaxial material testing. However, multiaxial material testing is time-consuming and requires specialized equipment. This study proposes two different deep neural network (DNN) approaches, a two- and three-dimensional convolutional neural network (DNN-2D and DNN-3D), to efficiently estimate biaxial stress-strain curves of aluminum alloy sheets from a digital image representing the sample's crystallographic texture. DNN-2D is designed to estimate biaxial stress-strain curves from a digital image of {111} pole figure, while DNN-3D estimates the curves from a 3D image of the texture. The two DNNs were trained using synthetic texture datasets and the corresponding biaxial stress-strain curves obtained from crystal plasticity-based numerical biaxial tensile tests. The accuracy of the two trained DNNs was examined by comparing the results from that of the numerical biaxial tensile tests. It was observed that both the DNNs could estimate biaxial stress-strain curves with high accuracy. Though DNN-3D provides with a better estimation than DNN-2D, it displays lower computational efficiency. Thus, the two DNNs and their training procedures offer a new and efficient method to provide virtual data for material modeling. Display omitted •DNNs were used to estimate biaxial stress-strain curves of aluminum alloy sheets.•Pole figure images and 3D orientation maps were explored as input data.•DNNs were as accurate as numerical biaxial tensile tests, but much faster.•A new approach to virtual data generation for material modeling was demonstrated.