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dc.contributor.authorYoujia, Wang-
dc.contributor.authorKai, He-
dc.contributor.authorTaotao, Zhou-
dc.date.accessioned2023-03-31T08:35:44Z-
dc.date.available2023-03-31T08:35:44Z-
dc.date.issued2023-
dc.identifier.urihttps://link.springer.com/article/10.1007/s11263-022-01730-5-
dc.identifier.urihttps://dlib.phenikaa-uni.edu.vn/handle/PNK/7398-
dc.descriptionCC BYvi
dc.description.abstractThe development of neural relighting techniques has by far outpaced the rate of their corresponding training data (e.g., OLAT) generation. For example, high-quality relighting from a single portrait image still requires supervision from comprehensive datasets covering broad diversities in gender, race, complexion, and facial geometry. We present a hybrid parametric neural relighting (PN-Relighting) framework for single portrait relighting, using a much smaller OLAT dataset or SMOLAT. At the core of PN-Relighting, we employ parametric 3D faces coupled with appearance inference and implicit material modelling to enrich SMOLAT for handling in-the-wild images.vi
dc.language.isoenvi
dc.publisherSpringervi
dc.subjectcorresponding training datavi
dc.subjectPN-Relightingvi
dc.titleFree-view Face Relighting Using a Hybrid Parametric Neural Model on a SMALL-OLAT Datasetvi
dc.typeBookvi
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