  Recently, video generation has achieved significant rapid development based
on superior text-to-image generation techniques. In this work, we propose a
high fidelity framework for image-to-video generation, named AtomoVideo. Based
on multi-granularity image injection, we achieve higher fidelity of the
generated video to the given image. In addition, thanks to high quality
datasets and training strategies, we achieve greater motion intensity while
maintaining superior temporal consistency and stability. Our architecture
extends flexibly to the video frame prediction task, enabling long sequence
prediction through iterative generation. Furthermore, due to the design of
adapter training, our approach can be well combined with existing personalized
models and controllable modules. By quantitatively and qualitatively
evaluation, AtomoVideo achieves superior results compared to popular methods,
more examples can be found on our project website:
https://atomo-video.github.io/.
