@misc{ReimannKlingbeilPasewaldtetal.2018,
  author    = {Reimann, Max and Klingbeil, Mandy and Pasewaldt, Sebastian and Semmo, Amir and Trapp, Matthias and D{\"o}llner, J{\"u}rgen Roland Friedrich},
  title     = {MaeSTrO: A Mobile App for Style Transfer Orchestration using Neural Networks},
  series = {International Conference on Cyberworlds (CW)},
  journal   = {International Conference on Cyberworlds (CW)},
  editor    = {Sourin, A Sourina},
  publisher = {IEEE},
  address   = {New York},
  isbn      = {978-1-5386-7315-7},
  doi       = {10.1109/CW.2018.00016},
  pages     = {9 -- 16},
  year      = {2018},
  abstract  = {Mobile expressive rendering gained increasing popularity among users seeking casual creativity by image stylization and supports the development of mobile artists as a new user group. In particular, neural style transfer has advanced as a core technology to emulate characteristics of manifold artistic styles. However, when it comes to creative expression, the technology still faces inherent limitations in providing low-level controls for localized image stylization. This work enhances state-of-the-art neural style transfer techniques by a generalized user interface with interactive tools to facilitate a creative and localized editing process. Thereby, we first propose a problem characterization representing trade-offs between visual quality, run-time performance, and user control. We then present MaeSTrO, a mobile app for orchestration of neural style transfer techniques using iterative, multi-style generative and adaptive neural networks that can be locally controlled by on-screen painting metaphors. At this, first user tests indicate different levels of satisfaction for the implemented techniques and interaction design.},
  language  = {en}
}