  Diffusion models have emerged as a powerful paradigm in video synthesis tasks
including prediction, generation, and interpolation. Due to the limitation of
the computational budget, existing methods usually implement conditional
diffusion models with an autoregressive inference pipeline, in which the future
fragment is predicted based on the distribution of adjacent past frames.
However, only the conditions from a few previous frames can't capture the
global temporal coherence, leading to inconsistent or even outrageous results
in long-term video prediction. In this paper, we propose a Local-Global Context
guided Video Diffusion model (LGC-VD) to capture multi-perception conditions
for producing high-quality videos in both conditional/unconditional settings.
In LGC-VD, the UNet is implemented with stacked residual blocks with
self-attention units, avoiding the undesirable computational cost in 3D Conv.
We construct a local-global context guidance strategy to capture the
multi-perceptual embedding of the past fragment to boost the consistency of
future prediction. Furthermore, we propose a two-stage training strategy to
alleviate the effect of noisy frames for more stable predictions. Our
experiments demonstrate that the proposed method achieves favorable performance
on video prediction, interpolation, and unconditional video generation. We
release code at https://github.com/exisas/LGC-VD.
