  We propose a new approach for high resolution semantic image synthesis. It
consists of one base image generator and multiple class-specific generators.
The base generator generates high quality images based on a segmentation map.
To further improve the quality of different objects, we create a bank of
Generative Adversarial Networks (GANs) by separately training class-specific
models. This has several benefits including -- dedicated weights for each
class; centrally aligned data for each model; additional training data from
other sources, potential of higher resolution and quality; and easy
manipulation of a specific object in the scene. Experiments show that our
approach can generate high quality images in high resolution while having
flexibility of object-level control by using class-specific generators.
