Edit One for All: Interactive Batch Image Editing

Authors: Thao Nguyen, Utkarsh Ojha, Yuheng Li, Haotian Liu, Yong Jae Lee

What

This paper introduces a novel method for interactive batch image editing using StyleGAN, enabling the automatic transfer of user-specified edits from an example image to a batch of test images while maintaining consistency in the final edited state.

Why

This paper addresses the limitations of existing image editing techniques that primarily focus on single-image editing. It introduces the concept of interactive batch image editing, which significantly reduces human effort and time required for editing large image datasets while ensuring consistent results across images.

How

The authors propose a two-stage approach. First, they model the user’s edit in the latent space of StyleGAN by optimizing an editing direction that captures the desired change while being globally consistent across images. Second, they derive a closed-form solution to adjust the editing strength for each test image, ensuring that all edited images converge to the same final state as the user-edited example.

Result

The proposed method demonstrates superior performance in transferring various edits, such as point-based dragging and text-driven modifications, across different object categories like faces, animals, and human bodies. It achieves comparable visual quality to state-of-the-art single-image editing methods while being significantly faster and requiring minimal user annotation.

LF

The authors acknowledge limitations in capturing fine-grained details and handling semantic discrepancies between the example and test images. Future work includes extending the approach to diffusion-based models for wider edit types and addressing limitations related to out-of-distribution samples.

Abstract

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.