  Recent improvements to Generative Adversarial Networks (GANs) have made it
possible to generate realistic images in high resolution based on natural
language descriptions such as image captions. Furthermore, conditional GANs
allow us to control the image generation process through labels or even natural
language descriptions. However, fine-grained control of the image layout, i.e.
where in the image specific objects should be located, is still difficult to
achieve. This is especially true for images that should contain multiple
distinct objects at different spatial locations. We introduce a new approach
which allows us to control the location of arbitrarily many objects within an
image by adding an object pathway to both the generator and the discriminator.
Our approach does not need a detailed semantic layout but only bounding boxes
and the respective labels of the desired objects are needed. The object pathway
focuses solely on the individual objects and is iteratively applied at the
locations specified by the bounding boxes. The global pathway focuses on the
image background and the general image layout. We perform experiments on the
Multi-MNIST, CLEVR, and the more complex MS-COCO data set. Our experiments show
that through the use of the object pathway we can control object locations
within images and can model complex scenes with multiple objects at various
locations. We further show that the object pathway focuses on the individual
objects and learns features relevant for these, while the global pathway
focuses on global image characteristics and the image background.
