GANgealing applies GAN-supervised learning to dense visual alignment, training a spatial transformer to warp unaligned GAN samples to a common target mode. The method jointly learns both the target mode and the alignment transform end-to-end. The model generalizes to real images after training exclusively on synthesized GAN data.