Harmonized Portrait‐Background Image Composition

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dc.contributor.authorWang, Yijiangen_US
dc.contributor.authorLi, Yuqien_US
dc.contributor.authorWang, Chongen_US
dc.contributor.authorYe, Xulunen_US
dc.contributor.editorHauser, Helwig and Alliez, Pierreen_US
dc.date.accessioned2023-10-06T11:58:52Z
dc.date.available2023-10-06T11:58:52Z
dc.date.issued2023
dc.description.abstractPortrait‐background image composition is a widely used operation in selfie editing, video meeting, and other portrait applications. To guarantee the realism of the composited images, the appearance of the foreground portraits needs to be adjusted to fit the new background images. Existing image harmonization approaches are proposed to handle general foreground objects, thus lack the special ability to adjust portrait foregrounds. In this paper, we present a novel end‐to‐end network architecture to learn both the content features and style features for portrait‐background composition. The method adjusts the appearance of portraits to make them compatible with backgrounds, while the generation of the composited images satisfies the prior of a style‐based generator. We also propose a pipeline to generate high‐quality and high‐variety synthesized image datasets for training and evaluation. The proposed method outperforms other state‐of‐the‐art methods both on the synthesized dataset and the real composited images and shows robust performance in video applications.en_US
dc.description.number6
dc.description.sectionheadersORIGINAL ARTICLES
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume42
dc.identifier.doi10.1111/cgf.14921
dc.identifier.issn1467-8659
dc.identifier.urihttps://doi.org/10.1111/cgf.14921
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14921
dc.publisher© 2023 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd.en_US
dc.subjectimage composition
dc.subjectportrait harmonization
dc.subjectstylegan
dc.titleHarmonized Portrait‐Background Image Compositionen_US
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