Animated Stickers: Bringing Stickers to Life with Video Diffusion
<|reference_start|>Animated Stickers: Bringing Stickers to Life with Video Diffusion: We introduce animated stickers, a video diffusion model which generates an animation conditioned on a text prompt and static sticker image. Our model is built on top of the state-of-the-art Emu text-to-image model, with the addition of temporal layers to model motion. Due to the domain gap, i.e. differences in visual and motion style, a model which performed well on generating natural videos can no longer generate vivid videos when applied to stickers. To bridge this gap, we employ a two-stage finetuning pipeline: first with weakly in-domain data, followed by human-in-the-loop (HITL) strategy which we term ensemble-of-teachers. It distills the best qualities of multiple teachers into a smaller student model. We show that this strategy allows us to specifically target improvements to motion quality while maintaining the style from the static image. With inference optimizations, our model is able to generate an eight-frame video with high-quality, interesting, and relevant motion in under one second.<|reference_end|>
@article{yan2024animated,
    title={Animated Stickers: Bringing Stickers to Life with Video Diffusion},
    author={David Yan, Winnie Zhang, Luxin Zhang, Anmol Kalia, Dingkang Wang,
  Ankit Ramchandani, Miao Liu, Albert Pumarola, Edgar Schoenfeld, Elliot
  Blanchard, Krishna Narni, Yaqiao Luo, Lawrence Chen, Guan Pang, Ali Thabet,
  Peter Vajda, Amy Bearman, Licheng Yu},
    journal={arXiv preprint arXiv:2402.06088},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.06088},
    primaryClass={cs.CV}
}