Generative Image Dynamics

Zhengqi Li, Richard Tucker, Noah Snavely, Aleksander Holynski

Generative Image Dynamics: 54 upvotes on Hugging Face Daily Papers, #1 of 7 papers on 2023-09-15. Day-by-day upvote history.

We present an approach to modeling an image-space prior on scene dynamics. Our prior is learned from a collection of motion trajectories extracted from real video sequences containing natural, oscillating motion such as trees, flowers, candles, and clothes blowing in the wind. Given a single image, our trained model uses a frequency-coordinated diffusion sampling process to predict a per-pixel long-term motion representation in the Fourier domain, which we call a neural stochastic motion texture. This representation can be converted into dense motion trajectories that span an entire video. Along with an image-based rendering module, these trajectories can be used for a number of downstream applications, such as turning still images into seamlessly looping dynamic videos, or allowing users to realistically interact with objects in real pictures.

Paper page on Hugging Face · arXiv

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