  We present a framework for training GANs with explicit control over generated
images. We are able to control the generated image by settings exact attributes
such as age, pose, expression, etc. Most approaches for editing GAN-generated
images achieve partial control by leveraging the latent space disentanglement
properties, obtained implicitly after standard GAN training. Such methods are
able to change the relative intensity of certain attributes, but not explicitly
set their values. Recently proposed methods, designed for explicit control over
human faces, harness morphable 3D face models to allow fine-grained control
capabilities in GANs. Unlike these methods, our control is not constrained to
morphable 3D face model parameters and is extendable beyond the domain of human
faces. Using contrastive learning, we obtain GANs with an explicitly
disentangled latent space. This disentanglement is utilized to train
control-encoders mapping human-interpretable inputs to suitable latent vectors,
thus allowing explicit control. In the domain of human faces we demonstrate
control over identity, age, pose, expression, hair color and illumination. We
also demonstrate control capabilities of our framework in the domains of
painted portraits and dog image generation. We demonstrate that our approach
achieves state-of-the-art performance both qualitatively and quantitatively.
