ale video datasets. In this paper, we introduce a new task of zero-shot text-to-video generation and propose a low-cost approach (without any training or optimization) by leveraging the power of existing text-to-image synthesis methods (e.g., Stable Diffusion), making them suitable for the video domain.
Our key modifications include (i) enriching the latent codes of the generated frames with motion dynamics to keep the global scene and the back