  Despite remarkable recent progress on both unconditional and conditional
image synthesis, it remains a long-standing problem to learn generative models
that are capable of synthesizing realistic and sharp images from reconfigurable
spatial layout (i.e., bounding boxes + class labels in an image lattice) and
style (i.e., structural and appearance variations encoded by latent vectors),
especially at high resolution. By reconfigurable, it means that a model can
preserve the intrinsic one-to-many mapping from a given layout to multiple
plausible images with different styles, and is adaptive with respect to
perturbations of a layout and style latent code. In this paper, we present a
layout- and style-based architecture for generative adversarial networks
(termed LostGANs) that can be trained end-to-end to generate images from
reconfigurable layout and style. Inspired by the vanilla StyleGAN, the proposed
LostGAN consists of two new components: (i) learning fine-grained mask maps in
a weakly-supervised manner to bridge the gap between layouts and images, and
(ii) learning object instance-specific layout-aware feature normalization
(ISLA-Norm) in the generator to realize multi-object style generation. In
experiments, the proposed method is tested on the COCO-Stuff dataset and the
Visual Genome dataset with state-of-the-art performance obtained. The code and
pretrained models are available at \url{https://github.com/iVMCL/LostGANs}.
