VideoCrafter2: Overcoming Data Limitations for High-Quality Video
  Diffusion Models
Haoxin Chen Yong Zhang Xiaodong Cun Menghan Xia Xintao Wang Chao Weng Ying Shan
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 noise, excellent details, and high aesthetic scores.
However, these models rely on large-scale, well-filtered, high-quality videos
that are not accessible to the community. Many existing research works, which
train models using the low-quality WebVid-10M dataset, struggle to generate
high-quality videos because the models are optimized to fit WebVid-10M. In this
work, we explore the training scheme of video models extended from Stable
Diffusion and investigate the feasibility of leveraging low-quality videos and
synthesized high-quality images to obtain a high-quality video model. We first
analyze the connection between the spatial and temporal modules of video models
and the distribution shift to low-quality videos. We observe that full training
of all modules results in a stronger coupling between spatial and temporal
modules than only training temporal modules. Based on this stronger coupling,
we shift the distribution to higher quality without motion degradation by
finetuning spatial modules with high-quality images, resulting in a generic
high-quality video model. Evaluations are conducted to demonstrate the
superiority of the proposed method, particularly in picture quality, motion,
and concept composition.