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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 …