HARIVO: Harnessing Text-to-Image Models for Video Generation
<|reference_start|>HARIVO: Harnessing Text-to-Image Models for Video Generation: We present a method to create diffusion-based video models from pretrained Text-to-Image (T2I) models. Recently, AnimateDiff proposed freezing the T2I model while only training temporal layers. We advance this method by proposing a unique architecture, incorporating a mapping network and frame-wise tokens, tailored for video generation while maintaining the diversity and creativity of the original T2I model. Key innovations include novel loss functions for temporal smoothness and a mitigating gradient sampling technique, ensuring realistic and temporally consistent video generation despite limited public video data. We have successfully integrated video-specific inductive biases into the architecture and loss functions. Our method, built on the frozen StableDiffusion model, simplifies training processes and allows for seamless integration with off-the-shelf models like ControlNet and DreamBooth. project page: https://kwonminki.github.io/HARIVO<|reference_end|>
@article{kwon2024harivo:,
    title={HARIVO: Harnessing Text-to-Image Models for Video Generation},
    author={Mingi Kwon, Seoung Wug Oh, Yang Zhou, Difan Liu, Joon-Young Lee,
  Haoran Cai, Baqiao Liu, Feng Liu, Youngjung Uh},
    journal={arXiv preprint arXiv:2410.07763},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.07763},
    primaryClass={cs.CV cs.AI}
}