Abstract Transformers are Deep Neural Networks (DNN) that utilize a self-attention
mechanism to capture contextual relationships within sequential data. Unlike traditional …

The recent wave of AI-generated content (AIGC) has witnessed substantial success in
computer vision, with the diffusion model playing a crucial role in this achievement. Due to …

By decomposing the image formation process into a sequential application of denoising
autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image …

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

Pre-training video transformers on extra large-scale datasets is generally required to
achieve premier performance on relatively small datasets. In this paper, we show that video …

Text-guided generative diffusion models unlock powerful image creation and editing tools.
Recent approaches that edit the content of footage while retaining structure require …

This work aims to learn a high-quality text-to-video (T2V) generative model by leveraging a
pre-trained text-to-image (T2I) model as a basis. It is a highly desirable yet challenging task …

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

A diffusion probabilistic model (DPM), which constructs a forward diffusion process by
gradually adding noise to data points and learns the reverse denoising process to generate …