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.

翻译：近年来，图像编辑技术取得了显著进展。随着人为控制能力的增强，我们现已能够以多种方式编辑图像——既可以通过文本指定要修改的内容，也可以基于交互式点操作直接拖拽图像中的元素。然而，现有研究大多聚焦于单张图像的编辑，关于如何同时编辑大规模图像批次的问题仍鲜有探讨。为最大程度减少编辑过程中的人工干预，本文提出了一种基于StyleGAN介质的交互式批量图像编辑新方法。用户对示例图像实施编辑操作（例如：将人脸转为正面）后，该方法可自动将该编辑效果迁移至其他测试图像，使它们无论初始状态（姿态）如何，最终都能达到相同的目标状态（例如：所有人脸均呈正面）。大量实验表明，本方法实现的编辑效果在视觉质量上与现有单图像编辑方法相当，同时具有更优的视觉一致性，并显著节省了时间和人力成本。