Perceptual Artifacts Localization for Image Synthesis Tasks
Lingzhi Zhang, Zhengjie Xu, Connelly Barnes, Yuqian Zhou, Qing Liu, He Zhang, Sohrab Amirghodsi, Zhe Lin
Abstract
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 invaluable downstream applications, such as automated artifact rectification, non-referential image quality evaluation, and abnormal region detection in images. The dataset and code are released here: https://owenzlz.github.io/PAL4VST
BibTeX
@inproceedings{iccv2023_perceptualartifa,
title = {Perceptual Artifacts Localization for Image Synthesis Tasks},
author = {Lingzhi Zhang and Zhengjie Xu and Connelly Barnes and Yuqian Zhou and Qing Liu and He Zhang and Sohrab Amirghodsi and Zhe Lin and Eli Shechtman and Jianbo Shi},
booktitle = {ICCV 2023},
year = {2023}
}