Deep Painting Harmonization

dc.contributor.authorLuan, Fujunen_US
dc.contributor.authorParis, Sylvainen_US
dc.contributor.authorShechtman, Elien_US
dc.contributor.authorBala, Kavitaen_US
dc.contributor.editorJakob, Wenzel and Hachisuka, Toshiyaen_US
dc.date.accessioned2018-07-01T07:22:46Z
dc.date.available2018-07-01T07:22:46Z
dc.date.issued2018
dc.description.abstractCopying 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.en_US
dc.description.number4
dc.description.sectionheadersImage-based Techniques
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume37
dc.identifier.doi10.1111/cgf.13478
dc.identifier.issn1467-8659
dc.identifier.pages95-106
dc.identifier.urihttps://doi.org/10.1111/cgf.13478
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf13478
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectComputing methodologies
dc.subjectImage processing
dc.titleDeep Painting Harmonizationen_US
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