  The evolution of video generation from text, starting with animating MNIST
numbers to simulating the physical world with Sora, has progressed at a
breakneck speed over the past seven years. While often seen as a superficial
expansion of the predecessor text-to-image generation model, text-to-video
generation models are developed upon carefully engineered constituents. Here,
we systematically discuss these elements consisting of but not limited to core
building blocks (vision, language, and temporal) and supporting features from
the perspective of their contributions to achieving a world model. We employ
the PRISMA framework to curate 97 impactful research articles from renowned
scientific databases primarily studying video synthesis using text conditions.
Upon minute exploration of these manuscripts, we observe that text-to-video
generation involves more intricate technologies beyond the plain extension of
text-to-image generation. Our additional review into the shortcomings of
Sora-generated videos pinpoints the call for more in-depth studies in various
enabling aspects of video generation such as dataset, evaluation metric,
efficient architecture, and human-controlled generation. Finally, we conclude
that the study of the text-to-video generation may still be in its infancy,
requiring contribution from the cross-discipline research community towards its
advancement as the first step to realize artificial general intelligence (AGI).
