Deep Painterly Harmonization

Copying an element from a photo and pasting it into a painting is a challenging task. Applying photo compositing techniques in this context yields subpar results that look like a collage — and existing painterly stylization algorithms, which are global, perform poorly when applied locally. We addres...

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Veröffentlicht in:Computer graphics forum Jg. 37; H. 4; S. 95 - 106
Hauptverfasser: Luan, Fujun, Paris, Sylvain, Shechtman, Eli, Bala, Kavita
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Blackwell Publishing Ltd 01.07.2018
Schlagworte:
Algorithms
CCS Concepts
Computing methodologies → Image processing
Copying
ISSN:0167-7055, 1467-8659
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:Copying an element from a photo and pasting it into a painting is a challenging task. Applying photo compositing techniques in this context yields subpar results that look like a collage — and existing painterly stylization algorithms, which are global, perform poorly when applied locally. We address these issues with a dedicated algorithm that carefully determines the local statistics to be transferred. We ensure both spatial and inter‐scale statistical consistency and demonstrate that both aspects are key to generating quality results. To cope with the diversity of ion levels and types of paintings, we introduce a technique to adjust the parameters of the transfer depending on the painting. We show that our algorithm produces significantly better results than photo compositing or global stylization techniques and that it enables creative painterly edits that would be otherwise difficult to achieve.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.13478