  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.
