Detecting and Correcting Perceptual Artifacts in Synthetic Face Images

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00377158" target="_blank" >RIV/68407700:21230/24:00377158 - isvavai.cz</a>

Výsledek na webu

<a href="https://cvww2024.sdrv.si/wp-content/uploads/sites/5/2024/02/CVWW2024_Proceedings.pdf" target="_blank" >https://cvww2024.sdrv.si/wp-content/uploads/sites/5/2024/02/CVWW2024_Proceedings.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Detecting and Correcting Perceptual Artifacts in Synthetic Face Images

Popis výsledku v původním jazyce

We propose a method for detecting and automatically correcting perceptual artifacts on synthetic face images. Recent generative models, such as diffusion models, can produce photorealistic images. However, these models often generate visual defects on the faces of people, especially at low resolutions, which impairs the quality of the images. We use a face detector and a binary classifier to identify perceptual artifacts. The classifier was trained on our dataset of manually annotated synthetic face images generated by a diffusion model, half of which contain perceptual artifacts. We compare our method with several baselines and show that it achieves superior accuracy of 93% on an independent test set. In addition, we propose a simple mechanism for automatically correcting the distorted faces using inpainting. For each face with artifact response, we generate several replacement candidates by inpainting and choose the best one by the lowest artifact score. The best candidate is then back-projected into to the image. Inpainting ensures a seamless connection between the corrected face and the original image. Our method improves the realism and quality of synthetic images.

Název v anglickém jazyce

Detecting and Correcting Perceptual Artifacts in Synthetic Face Images

Popis výsledku anglicky

We propose a method for detecting and automatically correcting perceptual artifacts on synthetic face images. Recent generative models, such as diffusion models, can produce photorealistic images. However, these models often generate visual defects on the faces of people, especially at low resolutions, which impairs the quality of the images. We use a face detector and a binary classifier to identify perceptual artifacts. The classifier was trained on our dataset of manually annotated synthetic face images generated by a diffusion model, half of which contain perceptual artifacts. We compare our method with several baselines and show that it achieves superior accuracy of 93% on an independent test set. In addition, we propose a simple mechanism for automatically correcting the distorted faces using inpainting. For each face with artifact response, we generate several replacement candidates by inpainting and choose the best one by the lowest artifact score. The best candidate is then back-projected into to the image. Inpainting ensures a seamless connection between the corrected face and the original image. Our method improves the realism and quality of synthetic images.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 27th Computer Vision Winter Workshop

ISBN

978-961-96564-0-2

ISSN

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e-ISSN

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Počet stran výsledku

9

Strana od-do

38-46

Název nakladatele

Slovenian Pattern Recognition Society

Místo vydání

Ljubljana

Místo konání akce

Terme Olimia

Datum konání akce

14. 2. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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