  Image diffusion models, trained on massive image collections, have emerged as
the most versatile image generator model in terms of quality and diversity.
They support inverting real images and conditional (e.g., text) generation,
making them attractive for high-quality image editing applications. We
investigate how to use such pre-trained image models for text-guided video
editing. The critical challenge is to achieve the target edits while still
preserving the content of the source video. Our method works in two simple
steps: first, we use a pre-trained structure-guided (e.g., depth) image
diffusion model to perform text-guided edits on an anchor frame; then, in the
key step, we progressively propagate the changes to the future frames via
self-attention feature injection to adapt the core denoising step of the
diffusion model. We then consolidate the changes by adjusting the latent code
for the frame before continuing the process. Our approach is training-free and
generalizes to a wide range of edits. We demonstrate the effectiveness of the
approach by extensive experimentation and compare it against four different
prior and parallel efforts (on ArXiv). We demonstrate that realistic
text-guided video edits are possible, without any compute-intensive
preprocessing or video-specific finetuning.

Computer Vision and Pattern Recognition