Deep generative models are a class of techniques that train deep neural networks to model
the distribution of training samples. Research has fragmented into various interconnected …

Variational autoencoders (VAEs) are powerful deep generative models widely used to
represent high-dimensional complex data through a low-dimensional latent space learned …

Abstract Latent Diffusion Models (LDMs) enable high-quality image synthesis while avoiding
excessive compute demands by training a diffusion model in a compressed lower …

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 present Stable Video Diffusion-a latent video diffusion model for high-resolution, state-of-
the-art text-to-video and image-to-video generation. Recently, latent diffusion models trained …

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 …

Generating temporally coherent high fidelity video is an important milestone in generative
modeling research. We make progress towards this milestone by proposing a diffusion …

Video prediction is a challenging task. The quality of video frames from current state-of-the-
art (SOTA) generative models tends to be poor and generalization beyond the training data …

We present a framework for video modeling based on denoising diffusion probabilistic
models that produces long-duration video completions in a variety of realistic environments …

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