@inproceedings{
daras2024warped,
title={Warped Diffusion: Solving Video Inverse Problems with Image Diffusion Models},
author={Giannis Daras and Weili Nie and Karsten Kreis and Alex Dimakis and Morteza Mardani and Nikola Borislavov Kovachki and Arash Vahdat},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=LH94zPv8cu}
}
Using image models naively for solving inverse video problems often suffers from flickering, texture-sticking, and temporal inconsistency in generated videos. To tackle these problems, in this paper, we view frames as continuous functions in the 2D space, and videos as a sequence of continuous warping transformations between different frames. This perspective allows us to train function space diffusion models only on **images** and utilize them to solve temporally correlated inverse problems. The function space diffusion models need to be equivariant with respect to the underlying spatial transformations. To ensure temporal consistency, we introduce a simple post-hoc test-time guidance towards (self)-equivariant solutions. Our method allows us to deploy state-of-the-art latent diffusion models such as Stable Diffusion XL to solve video inverse problems. We demonstrate the effectiveness of our method for video inpainting and $8\times$ video super-resolution, outperforming existing techniques based on noise transformations. We provide generated video results in the following URL: https://giannisdaras.github.io/warped_diffusion.github.io/.
Warped Diffusion: Solving Video Inverse Problems with Image Diffusion Models
Giannis Daras Weili Nie Karsten Kreis Alex Dimakis Morteza Mardani Nikola Borislavov Kovachki Arash Vahdat