Generative Inbetweening: Adapting Image-to-Video Models for Keyframe
  Interpolation
Authors: name: Xiaojuan Wang, name: Boyang Zhou, name: Brian Curless, name: Ira Kemelmacher-Shlizerman, name: Aleksander Holynski, name: Steven M. Seitz

We present a method for generating video sequences with coherent motion
between a pair of input key frames. We adapt a pretrained large-scale
image-to-video diffusion model (originally trained to generate videos moving
forward in time from a single input image) for key frame interpolation, i.e.,
to produce a video in between two input frames. We accomplish this adaptation
through a lightweight fine-tuning technique that produces a version of the
model that instead predicts videos moving backwards in time from a single input
image. This model (along with the original forward-moving model) is
subsequently used in a dual-directional diffusion sampling process that
combines the overlapping model estimates starting from each of the two
keyframes. Our experiments show that our method outperforms both existing
diffusion-based methods and traditional frame interpolation techniques.