  With the advent of generative adversarial networks, synthesizing images from
textual descriptions has recently become an active research area. It is a
flexible and intuitive way for conditional image generation with significant
progress in the last years regarding visual realism, diversity, and semantic
alignment. However, the field still faces several challenges that require
further research efforts such as enabling the generation of high-resolution
images with multiple objects, and developing suitable and reliable evaluation
metrics that correlate with human judgement. In this review, we contextualize
the state of the art of adversarial text-to-image synthesis models, their
development since their inception five years ago, and propose a taxonomy based
on the level of supervision. We critically examine current strategies to
evaluate text-to-image synthesis models, highlight shortcomings, and identify
new areas of research, ranging from the development of better datasets and
evaluation metrics to possible improvements in architectural design and model
training. This review complements previous surveys on generative adversarial
networks with a focus on text-to-image synthesis which we believe will help
researchers to further advance the field.
