Video-LaVIT: Unified Video-Language Pre-training with Decoupled
  Visual-Motional Tokenization
Authors: name: Yang Jin, name: Zhicheng Sun, name: Kun Xu, name: Kun Xu, name: Liwei Chen, name: Hao Jiang, name: Quzhe Huang, name: Chengru Song, name: Yuliang Liu, name: Di Zhang, name: Yang Song, name: Kun Gai, name: Yadong Mu

In light of recent advances in multimodal Large Language Models (LLMs), there
is increasing attention to scaling them from image-text data to more
informative real-world videos. Compared to static images, video poses unique
challenges for effective large-scale pre-training due to the modeling of its
spatiotemporal dynamics. In this paper, we address such limitations in
video-language pre-training with an efficient video decomposition that
represents each video as keyframes and temporal motions. These are then adapted
to an LLM using well-designed tokenizers that discretize visual and temporal
information as a few tokens, thus enabling unified generative pre-training of
videos, images, and text. At inference, the generated tokens from the LLM are
carefully recovered to the original continuous pixel space to create various
video content. Our proposed framework is both capable of comprehending and
generating image and video content, as demonstrated by its competitive
performance across 13 multimodal benchmarks in image and video understanding
and generation. Our code and models will be available at
https://video-lavit.github.io.