  We present MicroCinema, a straightforward yet effective framework for
high-quality and coherent text-to-video generation. Unlike existing approaches
that align text prompts with video directly, MicroCinema introduces a
Divide-and-Conquer strategy which divides the text-to-video into a two-stage
process: text-to-image generation and image\&text-to-video generation. This
strategy offers two significant advantages. a) It allows us to take full
advantage of the recent advances in text-to-image models, such as Stable
Diffusion, Midjourney, and DALLE, to generate photorealistic and highly
detailed images. b) Leveraging the generated image, the model can allocate less
focus to fine-grained appearance details, prioritizing the efficient learning
of motion dynamics. To implement this strategy effectively, we introduce two
core designs. First, we propose the Appearance Injection Network, enhancing the
preservation of the appearance of the given image. Second, we introduce the
Appearance Noise Prior, a novel mechanism aimed at maintaining the capabilities
of pre-trained 2D diffusion models. These design elements empower MicroCinema
to generate high-quality videos with precise motion, guided by the provided
text prompts. Extensive experiments demonstrate the superiority of the proposed
framework. Concretely, MicroCinema achieves SOTA zero-shot FVD of 342.86 on
UCF-101 and 377.40 on MSR-VTT. See
https://wangyanhui666.github.io/MicroCinema.github.io/ for video samples.
