  In many applications of computer graphics, art and design, it is desirable
for a user to provide intuitive non-image input, such as text, sketch, stroke,
graph or layout, and have a computer system automatically generate
photo-realistic images that adhere to the input content. While classic works
that allow such automatic image content generation have followed a framework of
image retrieval and composition, recent advances in deep generative models such
as generative adversarial networks (GANs), variational autoencoders (VAEs), and
flow-based methods have enabled more powerful and versatile image generation
tasks. This paper reviews recent works for image synthesis given intuitive user
input, covering advances in input versatility, image generation methodology,
benchmark datasets, and evaluation metrics. This motivates new perspectives on
input representation and interactivity, cross pollination between major image
generation paradigms, and evaluation and comparison of generation methods.
