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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| Vydáno v: | Computer graphics forum Ročník 37; číslo 4; s. 95 - 106 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Oxford
Blackwell Publishing Ltd
01.07.2018
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| ISSN: | 0167-7055, 1467-8659 |
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| Abstract | 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. |
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| AbstractList | 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 abstraction 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. 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. |
| Author | Luan, Fujun Paris, Sylvain Shechtman, Eli Bala, Kavita |
| Author_xml | – sequence: 1 givenname: Fujun surname: Luan fullname: Luan, Fujun organization: Cornell University – sequence: 2 givenname: Sylvain surname: Paris fullname: Paris, Sylvain organization: Adobe Research – sequence: 3 givenname: Eli surname: Shechtman fullname: Shechtman, Eli organization: Adobe Research – sequence: 4 givenname: Kavita surname: Bala fullname: Bala, Kavita organization: Cornell University |
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| Cites_doi | 10.1109/ICCV.2017.167 10.1109/CVPR.2017.397 10.1109/CVPR.2015.7299155 10.1145/1778765.1778788 10.1007/978-3-319-46475-6_43 10.1145/3072959.3073683 10.1145/964965.808606 10.1145/2816795.2818066 10.1145/882262.882269 10.1109/ICCV.2015.449 10.1109/CVPR.2017.299 10.1145/2185520.2185578 10.1007/978-3-642-15549-9_1 10.23915/distill.00003 10.1007/BF01589116 10.1145/1531326.1531330 10.1109/CVPR.2017.740 10.1145/1778765.1778862 10.1145/2185520.2185580 10.1007/978-3-319-46487-9_43 |
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| Copyright | 2018 The Author(s) Computer Graphics Forum © 2018 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd. 2018 The Eurographics Association and John Wiley & Sons Ltd. |
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| Snippet | 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... |
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| SubjectTerms | Algorithms CCS Concepts Computing methodologies → Image processing Copying |
| Title | Deep Painterly Harmonization |
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