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arXiv · 2408.15239

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

Xiaojuan Wang·Boyang Zhou·Brian Curless·Ira Kemelmacher-Shlizerman·Aleksander Holynski·Steven M. Seitz
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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.

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Xiaojuan Wang, Boyang Zhou, Brian Curless, Ira Kemelmacher-Shlizerman, Aleksander Holynski, Steven M. Seitz. 2025-02-12. Generative Inbetweening: Adapting Image-to-Video Models for Keyframe Interpolation. https://arxiv.org/abs/2408.15239

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