User-Guided Lip Correction for Facial Performance Capture

dc.contributor.authorDinev, Dimitaren_US
dc.contributor.authorBeeler, Thaboen_US
dc.contributor.authorBradley, Dereken_US
dc.contributor.authorBächer, Moritzen_US
dc.contributor.authorXu, Hongyien_US
dc.contributor.authorKavan, Ladislaven_US
dc.contributor.editorThuerey, Nils and Beeler, Thaboen_US
dc.date.accessioned2018-07-23T10:07:12Z
dc.date.available2018-07-23T10:07:12Z
dc.date.issued2018
dc.description.abstractFacial performance capture is the primary method for generating facial animation in video games, feature films and virtual environments, and recent advances have produced very compelling results. Still, one of the most challenging regions is the mouth, which often contains systematic errors due to the complex appearance and occlusion/dis-occlusion of the lips. We present a novel user-guided approach to correcting these common lip shape errors present in traditional capture systems. Our approach is to allow a user to manually correct a small number of problematic frames, and then our system learns the types of corrections desired and automatically corrects the entire performance. As correcting even a single frame using traditional 3D sculpting tools can be time consuming and require great skill, we also propose a simple and fast 2D sketch-based method for generating plausible lip corrections for the problematic key frames. We demonstrate our results on captured performances of three different subjects, and validate our method with an additional sequence that contains ground truth lip reconstructions.en_US
dc.description.number8
dc.description.sectionheadersHumans
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume37
dc.identifier.doi10.1111/cgf.13515
dc.identifier.issn1467-8659
dc.identifier.pages93-101
dc.identifier.urihttps://doi.org/10.1111/cgf.13515
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf13515
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.titleUser-Guided Lip Correction for Facial Performance Captureen_US
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