Recent advancements in deep generative models have facilitated the creation of photo-realistic images across various tasks. However, these generated images often exhibit perceptual artifacts in specific regions, necessitating manual correction. In this study, we present a comprehensive empirical examination of Perceptual Artifacts Localization (PAL) spanning diverse image synthesis endeavors. We introduce a novel dataset comprising 10,168 generated images, each annotated with per-pixel perceptual artifact labels across ten synthesis tasks. A segmentation model, trained on our proposed dataset, effectively localizes artifacts across a range of tasks. Additionally, we illustrate its proficiency in adapting to previously unseen models using minimal training samples. We further propose an innovative zoom-in inpainting pipeline that seamlessly rectifies perceptual artifacts in the generated images. Through our experimental analyses, we elucidate several practical downstream applications, such as automated artifact rectification, non-referential image quality evaluation, and abnormal region detection in images. The dataset and code are released.

翻译：近期深度生成模型的进步促进了多种任务中逼真图像的生成。然而，这些生成图像往往在特定区域存在感知伪影，需要人工修正。本研究对跨多种图像合成任务的感知伪影定位展开了全面的实证分析。我们提出一个包含10,168张生成图像的新数据集，每张图像在十种合成任务中均标注了逐像素的感知伪影标签。基于该数据集训练的语义分割模型能有效定位多种任务中的伪影。此外，我们展示了该模型仅需少量训练样本即可适应未见过的生成模型。进一步，我们提出了一种创新的放大修复流水线，可无缝校正生成图像中的感知伪影。通过实验分析，我们阐明了几种实际下游应用，如自动伪影校正、无参考图像质量评估以及图像异常区域检测。数据集与代码均已公开。