Perceptual Artifacts Localization for Image Synthesis Tasks

Lingzhi Zhang; Zhengjie Xu; Connelly Barnes; Yuqian Zhou; Qing Liu; He Zhang; Sohrab Amirghodsi; Zhe Lin; Eli Shechtman; Jianbo Shi

arXiv · 2310.05590

Perceptual Artifacts Localization for Image Synthesis Tasks

Lingzhi Zhang·Zhengjie Xu·Connelly Barnes·Yuqian Zhou·Qing Liu·He Zhang·Sohrab Amirghodsi·Zhe Lin·Eli Shechtman·Jianbo Shi

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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 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.

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BibTeX RIS

Lingzhi Zhang, Zhengjie Xu, Connelly Barnes, Yuqian Zhou, Qing Liu, He Zhang, Sohrab Amirghodsi, Zhe Lin, Eli Shechtman, Jianbo Shi. 2023-10-09. Perceptual Artifacts Localization for Image Synthesis Tasks. https://arxiv.org/abs/2310.05590

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