Generative Inbetweening: Adapting Image-to-Video Models for Keyframe Interpolation

TL;DR

This paper introduces a novel method for keyframe video interpolation by adapting a pretrained image-to-video diffusion model to generate coherent intermediate frames through dual-directional sampling, outperforming existing techniques.

Contribution

It presents a lightweight fine-tuning approach to adapt a large-scale diffusion model for bidirectional video interpolation, enhancing motion coherence between keyframes.

Findings

Outperforms existing diffusion-based interpolation methods

Achieves more coherent motion in generated videos

Demonstrates effectiveness on diverse video sequences

Abstract

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.