Recurrent neural networks (RNNs) have been widely adopted in research areas concerned
with sequential data, such as text, audio, and video. However, RNNs consisting of sigma …

In recent years, the image and video coding technologies have advanced by leaps and
bounds. However, due to the popularization of image and video acquisition devices, the …

Despite tremendous progress in generating high-quality images using diffusion models,
synthesizing a sequence of animated frames that are both photorealistic and temporally …

We present Imagen Video, a text-conditional video generation system based on a cascade
of video diffusion models. Given a text prompt, Imagen Video generates high definition …

We introduce a novel sequential modeling approach which enables learning a Large Vision
Model (LVM) without making use of any linguistic data. To do this we define a common …

World models, especially in autonomous driving, are trending and drawing extensive
attention due to their capacity for comprehending driving environments. The established …

We present Phenaki, a model capable of realistic video synthesis given a sequence of
textual prompts. Generating videos from text is particularly challenging due to the …

Abstract We present Emu Video, a text-to-video generation model that factorizes the
generation into two steps: first generating an image conditioned on the text, and then …

Abstract From CNN, RNN, to ViT, we have witnessed remarkable advancements in video
prediction, incorporating auxiliary inputs, elaborate neural architectures, and sophisticated …

Videos show continuous events, yet most--if not all--video synthesis frameworks treat them
discretely in time. In this work, we think of videos of what they should be--time-continuous …