  Generative Adversarial Networks (GANs) have made great success in
synthesizing high-quality images. However, how to steer the generation process
of a well-trained GAN model and customize the output image is much less
explored. It has been recently found that modulating the input latent code used
in GANs can reasonably alter some variation factors in the output image, but
such manipulation usually presents to change the entire image as a whole. In
this work, we propose an effective approach, termed as LoGAN, to support local
editing of the output image. Concretely, we introduce two operators, i.e.,
content modulation and style modulation, together with a priority mask to
facilitate the precise control of the intermediate generative features. Taking
bedroom synthesis as an instance, we are able to seamlessly remove, insert,
shift, and rotate the individual objects inside a room. Furthermore, our method
can completely clear out a room and then refurnish it with customized furniture
and styles. Experimental results show the great potentials of steering the
image generation of pre-trained GANs for versatile image editing.
