UV Completion with Self-referenced Discrimination

dc.contributor.authorKang, Jiwooen_US
dc.contributor.authorLee, Seongminen_US
dc.contributor.authorLee, Sanghoonen_US
dc.contributor.editorWilkie, Alexander and Banterle, Francescoen_US
dc.date.accessioned2020-05-24T13:43:04Z
dc.date.available2020-05-24T13:43:04Z
dc.date.issued2020
dc.description.abstractA facial UV map is used in many applications such as facial reconstruction, synthesis, recognition, and editing. However, it is difficult to collect a number of the UVs needed for accuracy using 3D scan device, or a multi-view capturing system should be required to construct the UV. An occluded facial UV with holes could be obtained by sampling an image after fitting a 3D facial model by recent alignment methods. In this paper, we introduce a facial UV completion framework to train the deep neural network with a set of incomplete UV textures. By using the fact that the facial texture distributions of the left and right half-sides are almost equal, we devise an adversarial network to model the complete UV distribution of the facial texture. Also, we propose the self-referenced discrimination scheme that uses the facial UV completed from the generator for training real distribution. It is demonstrated that the network can be trained to complete the facial texture with incomplete UVs comparably to when utilizing the ground-truth UVs.en_US
dc.description.sectionheadersModelling - Appearance
dc.description.seriesinformationEurographics 2020 - Short Papers
dc.identifier.doi10.2312/egs.20201018
dc.identifier.isbn978-3-03868-101-4
dc.identifier.issn1017-4656
dc.identifier.pages61-64
dc.identifier.urihttps://doi.org/10.2312/egs.20201018
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/egs20201018
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/]
dc.subjectComputing methodologies
dc.subjectImage processing
dc.subjectNeural networks
dc.titleUV Completion with Self-referenced Discriminationen_US
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