As information exists in various modalities in real world, effective interaction and fusion
among multimodal information plays a key role for the creation and perception of multimodal …

Development of computer vision algorithms using convolutional neural networks and deep
learning has necessitated ever greater amounts of annotated and labelled data to produce …

To replicate the success of text-to-image (T2I) generation, recent works employ large-scale
video datasets to train a text-to-video (T2V) generator. Despite their promising results, such …

We propose Make-A-Video--an approach for directly translating the tremendous recent
progress in Text-to-Image (T2I) generation to Text-to-Video (T2V). Our intuition is simple …

Abstract We present the Pathways [1] Autoregressive Text-to-Image (Parti) model, which
generates high-fidelity photorealistic images and supports content-rich synthesis involving …

We present Muse, a text-to-image Transformer model that achieves state-of-the-art image
generation performance while being significantly more efficient than diffusion or …

We present Imagen, a text-to-image diffusion model with an unprecedented degree of
photorealism and a deep level of language understanding. Imagen builds on the power of …

We introduce AudioLM, a framework for high-quality audio generation with long-term
consistency. AudioLM maps the input audio to a sequence of discrete tokens and casts …

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 …

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 …