Edit One for All：交互式批量图像编辑
Edit One for All: Interactive Batch Image Editing
AI总结：
针对批量图像需逐张编辑的问题，本文提出以 StyleGAN 为媒介，将用户在单张示例图上指定的交互式编辑自动迁移到整批图像，在保持视觉质量的同时提升一致性并节省人力。
摘要：
近年来，图像编辑取得了显著进展。随着人工控制能力的增强，如今已经能够以多种多样的方式编辑图像：从用文字说明想要更改的内容，到以交互式的基于点的方式直接拖动图像内容。然而，大多数关注点仍停留在一次编辑单张图像上。我们是否能够以及如何同时编辑大批量图像，仍然缺乏充分研究。为了最大限度减少编辑过程中的人工监督，本文提出了一种以 StyleGAN 为媒介的新型交互式批量图像编辑方法。给定用户在一张示例图像中指定的编辑（例如，使面部转正），我们的方法能够自动将该编辑迁移到其他测试图像，从而无论这些图像的初始状态（姿态）如何，它们都会达到相同的最终状态（例如，全部面向前方）。大量实验表明，使用我们的方法进行的编辑在视觉质量上与现有单图像编辑方法相近，同时具有更高的视觉一致性，并显著节省时间和人力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
