  Video generation is a rapidly advancing research area, garnering significant
attention due to its broad range of applications. One critical aspect of this
field is the generation of long-duration videos, which presents unique
challenges and opportunities. This paper presents the first survey of recent
advancements in long video generation and summarises them into two key
paradigms: divide and conquer temporal autoregressive.
  We delve into the common models employed in each paradigm, including aspects
of network design and conditioning techniques. Furthermore, we offer a
comprehensive overview and classification of the datasets and evaluation
metrics which are crucial for advancing long video generation research.
Concluding with a summary of existing studies, we also discuss the emerging
challenges and future directions in this dynamic field. We hope that this
survey will serve as an essential reference for researchers and practitioners
in the realm of long video generation.
