Pathways on the Image Manifold: Image Editing via Video Generation
Authors: name: Noam Rotstein, name: Gal Yona, name: Daniel Silver, name: Roy Velich, name: David Bensaïd, name: Ron Kimmel

Recent advances in image editing, driven by image diffusion models, have
shown remarkable progress. However, significant challenges remain, as these
models often struggle to follow complex edit instructions accurately and
frequently compromise fidelity by altering key elements of the original image.
Simultaneously, video generation has made remarkable strides, with models that
effectively function as consistent and continuous world simulators. In this
paper, we propose merging these two fields by utilizing image-to-video models
for image editing. We reformulate image editing as a temporal process, using
pretrained video models to create smooth transitions from the original image to
the desired edit. This approach traverses the image manifold continuously,
ensuring consistent edits while preserving the original image's key aspects.
Our approach achieves state-of-the-art results on text-based image editing,
demonstrating significant improvements in both edit accuracy and image
preservation.