  Diffusion models have achieved great success in image generation. However,
when leveraging this idea for video generation, we face significant challenges
in maintaining the consistency and continuity across video frames. This is
mainly caused by the lack of an effective framework to align frames of videos
with desired temporal features while preserving consistent semantic and
stochastic features. In this work, we propose a novel Sector-Shaped Diffusion
Model (S2DM) whose sector-shaped diffusion region is formed by a set of
ray-shaped reverse diffusion processes starting at the same noise point. S2DM
can generate a group of intrinsically related data sharing the same semantic
and stochastic features while varying on temporal features with appropriate
guided conditions. We apply S2DM to video generation tasks, and explore the use
of optical flow as temporal conditions. Our experimental results show that S2DM
outperforms many existing methods in the task of video generation without any
temporal-feature modelling modules. For text-to-video generation tasks where
temporal conditions are not explicitly given, we propose a two-stage generation
strategy which can decouple the generation of temporal features from
semantic-content features. We show that, without additional training, our model
integrated with another temporal conditions generative model can still achieve
comparable performance with existing works. Our results can be viewd at
https://s2dm.github.io/S2DM/.
