Edit One for All: Interactive Batch Image Editing
Thao Nguyen Utkarsh Ojha Yuheng Li Haotian Liu Yong Jae Lee
In recent years, image editing has advanced remarkably. With increased human
control, it is now possible to edit an image in a plethora of ways; from
specifying in text what we want to change, to straight up dragging the contents
of the image in an interactive point-based manner. However, most of the focus
has remained on editing single images at a time. Whether and how we can
simultaneously edit large batches of images has remained understudied. With the
goal of minimizing human supervision in the editing process, this paper
presents a novel method for interactive batch image editing using StyleGAN as
the medium. Given an edit specified by users in an example image (e.g., make
the face frontal), our method can automatically transfer that edit to other
test images, so that regardless of their initial state (pose), they all arrive
at the same final state (e.g., all facing front). Extensive experiments
demonstrate that edits performed using our method have similar visual quality
to existing single-image-editing methods, while having more visual consistency
and saving significant time and human effort.