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G3AN: Disentangling Appearance and Motion for Video Generation

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dc.abstract.enCreating realistic human videos entails the challenge of being able to simultaneously generate both appearance, as well as motion. To tackle this challenge, we introduce G3AN, a novel spatio-temporal generative model, which seeks to capture the distribution of high dimensional video data and to model appearance and motion in disentangled manner. The latter is achieved by decomposing appearance and motion in a three-stream Generator, where the main stream aims to model spatio-temporal consistency, whereas the two auxiliary streams augment the main stream with multi-scale appearance and motion features, respectively. An extensive quantitative and qualitative analysis shows that our model systematically and significantly outperforms state-of-the-art methods on the facial expression datasets MUG and UvA-NEMO, as well as the Weizmann and UCF101 datasets on human action. Additional analysis on the learned latent representations confirms the successful decomposition of appearance and motion.
dc.affiliationUniwersytet Warszawski
dc.conference.countryStany Zjednoczone
dc.conference.datefinish2020-06-19
dc.conference.datestart2020-06-14
dc.conference.placeSeattle
dc.conference.seriesIEEE Conference on Computer Vision and Pattern Recognition
dc.conference.seriesIEEE Conference on Computer Vision and Pattern Recognition
dc.conference.seriesshortcutCVPR
dc.conference.shortcutCVPR 2020
dc.conference.weblinkhttp://cvpr2020.thecvf.com/
dc.contributor.authorWang, Yaohui
dc.contributor.authorDantcheva, Antitza
dc.contributor.authorBremond, Francois
dc.contributor.authorBiliński, Piotr
dc.date.accessioned2024-01-25T01:42:45Z
dc.date.available2024-01-25T01:42:45Z
dc.date.issued2020
dc.description.financePublikacja bezkosztowa
dc.identifier.doi10.1109/CVPR42600.2020.00531
dc.identifier.urihttps://repozytorium.uw.edu.pl//handle/item/107674
dc.identifier.weblinkhttp://xplorestaging.ieee.org/ielx7/9142308/9156271/09157816.pdf?arnumber=9157816
dc.languageeng
dc.pbn.affiliationcomputer and information sciences
dc.relation.pages1-10
dc.rightsClosedAccess
dc.sciencecloudnosend
dc.titleG3AN: Disentangling Appearance and Motion for Video Generation
dc.typeJournalArticle
dspace.entity.typePublication