Factorized-Dreamer: Training A High-Quality Video Generator with Limited
  and Low-Quality Data
Authors: name: Tao Yang, name: Yangming Shi, name: Yunwen Huang, name: Feng Chen, name: Yin Zheng, name: Lei Zhang

Text-to-video (T2V) generation has gained significant attention due to its
wide applications to video generation, editing, enhancement and translation,
\etc. However, high-quality (HQ) video synthesis is extremely challenging
because of the diverse and complex motions existed in real world. Most existing
works struggle to address this problem by collecting large-scale HQ videos,
which are inaccessible to the community. In this work, we show that publicly
available limited and low-quality (LQ) data are sufficient to train a HQ video
generator without recaptioning or finetuning. We factorize the whole T2V
generation process into two steps: generating an image conditioned on a highly
descriptive caption, and synthesizing the video conditioned on the generated
image and a concise caption of motion details. Specifically, we present
Factorized-Dreamer, a factorized spatiotemporal framework with several
critical designs for T2V generation, including an adapter to combine text and
image embeddings, a pixel-aware cross attention module to capture pixel-level
image information, a T5 text encoder to better understand motion description,
and a PredictNet to supervise optical flows. We further present a noise
schedule, which plays a key role in ensuring the quality and stability of video
generation. Our model lowers the requirements in detailed captions and HQ
videos, and can be directly trained on limited LQ datasets with noisy and brief
captions such as WebVid-10M, largely alleviating the cost to collect
large-scale HQ video-text pairs. Extensive experiments in a variety of T2V and
image-to-video generation tasks demonstrate the effectiveness of our proposed
Factorized-Dreamer. Our source codes are available at
https://github.com/yangxy/Factorized-Dreamer/.