  This work presents an easy-to-use regularizer for GAN training, which helps
explicitly link some axes of the latent space to a set of pixels in the
synthesized image. Establishing such a connection facilitates a more convenient
local control of GAN generation, where users can alter the image content only
within a spatial area simply by partially resampling the latent code.
Experimental results confirm four appealing properties of our regularizer,
which we call LinkGAN. (1) The latent-pixel linkage is applicable to either a
fixed region (\textit{i.e.}, same for all instances) or a particular semantic
category (i.e., varying across instances), like the sky. (2) Two or multiple
regions can be independently linked to different latent axes, which further
supports joint control. (3) Our regularizer can improve the spatial
controllability of both 2D and 3D-aware GAN models, barely sacrificing the
synthesis performance. (4) The models trained with our regularizer are
compatible with GAN inversion techniques and maintain editability on real
images.
