Video Outpainting using Conditional Generative Adverarial Networks

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dc.contributor.advisorHougen, Dean
dc.contributor.authorMaggia, Braiden
dc.contributor.committeeMemberJabrzemski, Rafal
dc.contributor.committeeMemberGrant, Christan
dc.date.accessioned2021-08-06T13:24:43Z
dc.date.available2021-08-06T13:24:43Z
dc.date.issued2021
dc.date.manuscript2021-08-05
dc.description.abstractRecent advancements in machine learning and neural networks have pushed the boundaries of what computers can achieve. Generative adversarial networks are a specific type of neural network that have proved wildly successful at content generation tasks. With this success, filling in missing sections of images or videos became a research topic of interest. Research in video inpainting has made steady progress throughout the years focusing on filling missing content in the center of a frame while research on video outpainting, which focuses on filling missing sections on the edge of the frame, has not. This thesis focuses on outpainting research by using conditional generative adversarial networks (cGANs) which apply a condition, such as an input image, to a generative adversarial network (GAN) in order to reformat traditional 4:3 video into a modern 16:9 format. This is accomplished by using a cGAN typically used for image-to-image translation and adapting it to generate the missing content from video frames. Although generated frames are not capable of accurately reconstructing missing content, this process is capable of producing context aware video that many times seamlessly blends with the original frame. The results of this research provide a glimpse of the possibility of using conditional generative adversarial networks for video outpainting.en_US
dc.identifier.urihttps://hdl.handle.net/11244/330234
dc.languageen_USen_US
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectMachine Learningen_US
dc.subjectGenerative Adversarial Networksen_US
dc.subjectOutpaintingen_US
dc.thesis.degreeMaster of Scienceen_US
dc.titleVideo Outpainting using Conditional Generative Adverarial Networksen_US
ou.groupGallogly College of Engineering::School of Computer Scienceen_US
shareok.orcidhttps://orcid.org/0000-0003-3074-8266en_US

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