Approaches for Local Artistic Control of Mobile Neural Style Transfer

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dc.contributor.authorReimann, Maxen_US
dc.contributor.authorKlingbeil, Mandyen_US
dc.contributor.authorPasewaldt, Sebastianen_US
dc.contributor.authorSemmo, Amiren_US
dc.contributor.authorDöllner, Jürgenen_US
dc.contributor.authorTrapp, Matthiasen_US
dc.contributor.editorAydın, Tunç and Sýkora, Danielen_US
dc.date.accessioned2018-11-10T20:57:27Z
dc.date.available2018-11-10T20:57:27Z
dc.date.issued2018
dc.description.abstractThis work presents enhancements to state-of-the-art adaptive neural style transfer techniques, thereby providing a generalized user interface with creativity tool support for lower-level local control to facilitate the demanding interactive editing on mobile devices. The approaches are implemented in a mobile app that is designed for orchestration of three neural style transfer techniques using iterative, multi-style generative and adaptive neural networks that can be locally controlled by on-screen painting metaphors to perform location-based filtering and direct the composition. Based on first user tests, we conclude with insights, showing different levels of satisfaction for the implemented techniques and user interaction design, pointing out directions for future research.en_US
dc.description.sectionheadersPosters
dc.description.seriesinformationExpressive: Computational Aesthetics, Sketch-Based Interfaces and Modeling, Non-Photorealistic Animation and Rendering
dc.identifier.doi10.1145/3229147.3229188
dc.identifier.isbn978-1-4503-5892-7
dc.identifier.issn2079-8679
dc.identifier.urihttps://doi.org/10.1145/3229147.3229188
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1145/3229147-3229188
dc.publisherACMen_US
dc.titleApproaches for Local Artistic Control of Mobile Neural Style Transferen_US
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