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 …

The field of visual computing is rapidly advancing due to the emergence of generative
artificial intelligence (AI), which unlocks unprecedented capabilities for the generation …

Text-to-video generation aims to produce a video based on a given prompt. Recently
several commercial video models have been able to generate plausible videos with minimal …

Image diffusion models, trained on massive image collections, have emerged as the most
versatile image generator model in terms of quality and diversity. They support inverting real …

Diffusion-based methods can generate realistic images and videos, but they struggle to edit
existing objects in a video while preserving their geometry over time. This prevents diffusion …

We present WALT, a diffusion transformer for photorealistic video generation from text
prompts. Our approach has two key design decisions. First, we use a causal encoder to …

We present VideoPoet, a language model capable of synthesizing high-quality video, with
matching audio, from a large variety of conditioning signals. VideoPoet employs a decoder …

We propose a novel Latent Diffusion Transformer, namely Latte, for video generation. Latte
first extracts spatio-temporal tokens from input videos and then adopts a series of …

AI-generated content has attracted lots of attention recently, but photo-realistic video
synthesis is still challenging. Although many attempts using GANs and autoregressive …

Large-scale pre-trained diffusion models have exhibited remarkable capabilities in diverse
video generations. Given a set of video clips of the same motion concept, the task of Motion …