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dc.contributor.authorLi, Yaoen_US
dc.contributor.authorYu, Xiang Gangen_US
dc.contributor.authorHan, Xiao Guangen_US
dc.contributor.authorJiang, Nian Juanen_US
dc.contributor.authorJia, Kuien_US
dc.contributor.authorLu, Jiang Boen_US
dc.contributor.editorLee, Sung-hee and Zollmann, Stefanie and Okabe, Makoto and Wuensche, Burkharden_US
dc.date.accessioned2020-10-29T18:39:33Z
dc.date.available2020-10-29T18:39:33Z
dc.date.issued2020
dc.identifier.isbn978-3-03868-120-5
dc.identifier.urihttps://doi.org/10.2312/pg.20201224
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/pg20201224
dc.description.abstractIn this work, we propose an interactive system to design diverse high-quality garment images from fashion sketches and the texture information. The major challenge behind this system is to generate high-quality and detailed texture according to the user-provided texture information. Prior works mainly use the texture patch representation and try to map a small texture patch to a whole garment image, hence unable to generate high-quality details. In contrast, inspired by intrinsic image decomposition, we decompose this task into texture synthesis and shading enhancement. In particular, we propose a novel bi-colored edge texture representation to synthesize textured garment images and a shading enhancer to render shading based on the grayscale edges. The bi-colored edge representation provides simple but effective texture cues and color constraints, so that the details can be better reconstructed. Moreover, with the rendered shading, the synthesized garment image becomes more vivid.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectNetworks
dc.subjectNetwork reliability
dc.subjectComputing methodologies
dc.subjectComputer vision
dc.titleA Deep Learning Based Interactive Sketching System for Fashion Images Designen_US
dc.description.seriesinformationPacific Graphics Short Papers, Posters, and Work-in-Progress Papers
dc.description.sectionheadersCreation and Reconstruction
dc.identifier.doi10.2312/pg.20201224
dc.identifier.pages13-18


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