Astounding results from Transformer models on natural language tasks have intrigued the
vision community to study their application to computer vision problems. Among their salient …

Human activity recognition is a key to a lot of applications such as healthcare and smart
home. In this study, we provide a comprehensive survey on recent advances and challenges …

Abstract We present Ego-Exo4D a diverse large-scale multimodal multiview video dataset
and benchmark challenge. Ego-Exo4D centers around simultaneously-captured egocentric …

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 …

Despite the remarkable progress in deep generative models, synthesizing high-resolution
and temporally coherent videos still remains a challenge due to their high-dimensionality …

Abstract We introduce the MAsked Generative VIdeo Transformer, MAGVIT, to tackle various
video synthesis tasks with a single model. We introduce a 3D tokenizer to quantize a video …

We introduce LAVILA, a new approach to learning video-language representations by
leveraging Large Language Models (LLMs). We repurpose pre-trained LLMs to be …

We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It
offers 3,670 hours of daily-life activity video spanning hundreds of scenarios (household …

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 We present All-Pairs Multi-Field Transforms (AMT), a new network architecture for
video frame interpolation. It is based on two essential designs. First, we build bidirectional …