  In this work, we study the image transformation problem by learning the
underlying transformations from a collection of images using Generative
Adversarial Networks (GANs). Specifically, we propose an unsupervised learning
framework, termed as TrGAN, to project images onto a transformation space that
is shared by the generator and the discriminator. Any two points in this
projected space define a transformation that can guide the image generation
process, leading to continuous semantic change. By projecting a pair of images
onto the transformation space, we are able to adequately extract the semantic
variation between them and further apply the extracted semantic to facilitating
image editing, including not only transferring image styles (e.g., changing day
to night) but also manipulating image contents (e.g., adding clouds in the
sky). Code and models are available at https://genforce.github.io/trgan.
