  Image editing serves as a practical yet challenging task considering the
diverse demands from users, where one of the hardest parts is to precisely
describe how the edited image should look like. In this work, we present a new
form of editing, termed imitative editing, to help users exercise their
creativity more conveniently. Concretely, to edit an image region of interest,
users are free to directly draw inspiration from some in-the-wild references
(e.g., some relative pictures come across online), without having to cope with
the fit between the reference and the source. Such a design requires the system
to automatically figure out what to expect from the reference to perform the
editing. For this purpose, we propose a generative training framework, dubbed
MimicBrush, which randomly selects two frames from a video clip, masks some
regions of one frame, and learns to recover the masked regions using the
information from the other frame. That way, our model, developed from a
diffusion prior, is able to capture the semantic correspondence between
separate images in a self-supervised manner. We experimentally show the
effectiveness of our method under various test cases as well as its superiority
over existing alternatives. We also construct a benchmark to facilitate further
research.
