Locally controllable neural style transfer on mobile devices

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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. In this work, we first propose a problem characterization of interactive style transfer representing a trade-off 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, we enhance state-of-the-art neural style transfer techniques by mask-based loss terms that can be interactively parameterized by a generalized user interface to facilitate a creative and localized editing process. We report on a usability study and an online survey that demonstrate the ability of our app to transfer styles at improved semantic plausibility.

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We would like to thank the anonymous reviewers for their valuable feedback. This work was funded by the Federal Ministry of Education and Research (BMBF), Germany, for the AVA project 01IS15041.

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Correspondence to Max Reimann.

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Reimann, M., Klingbeil, M., Pasewaldt, S. et al. Locally controllable neural style transfer on mobile devices. Vis Comput 35, 1531–1547 (2019). https://doi.org/10.1007/s00371-019-01654-1

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  • Non-photorealistic rendering
  • Style transfer
  • Neural networks
  • Mobile devices
  • Interactive control
  • Expressive rendering