  With impressive achievements made, artificial intelligence is on the path
forward to artificial general intelligence. Sora, developed by OpenAI, which is
capable of minute-level world-simulative abilities can be considered as a
milestone on this developmental path. However, despite its notable successes,
Sora still encounters various obstacles that need to be resolved. In this
survey, we embark from the perspective of disassembling Sora in text-to-video
generation, and conducting a comprehensive review of literature, trying to
answer the question, \textit{From Sora What We Can See}. Specifically, after
basic preliminaries regarding the general algorithms are introduced, the
literature is categorized from three mutually perpendicular dimensions:
evolutionary generators, excellent pursuit, and realistic panorama.
Subsequently, the widely used datasets and metrics are organized in detail.
Last but more importantly, we identify several challenges and open problems in
this domain and propose potential future directions for research and
development.
