Abstract: Disclosed implementations include a depth generation method using a novel teacher-student GAN architecture (TS-GAN) to generate depth images for 2-D images, such as RGB images, where no corresponding depth information is available. An example model consists of two components, a teacher and a student. The teacher consists of a fully convolutional encoder-decoder network as a generator along with a fully convolution classification network as the discriminator. The generator takes 2-D images as inputs and aims to output the corresponding depth images. The teacher learns an initial latent mapping between 2-dimensional and co-registered depth images and the student applies the latent mapping to provide feedback to the classification network for refinement.