Generative Inbetweening: Adapting Image-to-Video Models for Keyframe Interpolation
Xiaojuan Wang Boyang Zhou Brian Curless Ira Kemelmacher-Shlizerman Aleksander Holynski Steve Seitz
We present a method for generating video sequences with coherent motion between a pair of input keyframes. 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 keyframe interpolation, i.e., to produce a video 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 shows that our method outperforms both existing diffusion-based methods and traditional frame interpolation techniques.
generative keyframe interpolation, image-to-video diffusion models