InspireMePosing: Learn Pose and Composition from Portrait Examples

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dc.contributor.authorSheng, Binen_US
dc.contributor.authorJin, Yuxien_US
dc.contributor.authorLi, Pingen_US
dc.contributor.authorWang, Wenxiaoen_US
dc.contributor.authorFu, Hongboen_US
dc.contributor.authorWu, Enhuaen_US
dc.contributor.editorFu, Hongbo and Ghosh, Abhijeet and Kopf, Johannesen_US
dc.date.accessioned2018-10-07T14:32:05Z
dc.date.available2018-10-07T14:32:05Z
dc.date.issued2018
dc.description.abstractSince people tend to build relationship with others by personal photography, capturing high quality photographs on mobile device has become a strong demand. We propose a portrait photography guidance system to guide user's photographing. We consider current scene image as our input and find professional photograph examples with similar aesthetic features for it. Deep residual network is introduced to gather scene classification information and represent common photograph rules by features, and random forest is adopted to establishing mapping relations between extracted features and examples. Besides, we implement our guidance system on a camera application and evaluate it by user study.en_US
dc.description.sectionheadersTowards Better Quality of Images/Videos
dc.description.seriesinformationPacific Graphics Short Papers
dc.identifier.doi10.2312/pg.20181274
dc.identifier.isbn978-3-03868-073-4
dc.identifier.pages33-35
dc.identifier.urihttps://doi.org/10.2312/pg.20181274
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/pg20181274
dc.publisherThe Eurographics Associationen_US
dc.subjectComputing methodologies
dc.subjectImage manipulation
dc.subjectImage processing
dc.subjectApplied computing
dc.subjectArts and humanities
dc.titleInspireMePosing: Learn Pose and Composition from Portrait Examplesen_US
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