  We review research on generating visual data from text from the angle of
"cross-modal generation." This point of view allows us to draw parallels
between various methods geared towards working on input text and producing
visual output, without limiting the analysis to narrow sub-areas. It also
results in the identification of common templates in the field, which are then
compared and contrasted both within pools of similar methods and across lines
of research. We provide a breakdown of text-to-image generation into various
flavors of image-from-text methods, video-from-text methods, image editing,
self-supervised and graph-based approaches. In this discussion, we focus on
research papers published at 8 leading machine learning conferences in the
years 2016-2022, also incorporating a number of relevant papers not matching
the outlined search criteria. The conducted review suggests a significant
increase in the number of papers published in the area and highlights research
gaps and potential lines of investigation. To our knowledge, this is the first
review to systematically look at text-to-image generation from the perspective
of "cross-modal generation."
