@article{ReimannBuchheimSemmoetal.2022,
  author    = {Reimann, Max and Buchheim, Benito and Semmo, Amir and D{\"o}llner, J{\"u}rgen and Trapp, Matthias},
  title     = {Controlling strokes in fast neural style transfer using content transforms},
  series = {The Visual Computer},
  volume    = {38},
  journal   = {The Visual Computer},
  number    = {12},
  publisher = {Springer},
  address   = {New York},
  issn      = {0178-2789},
  doi       = {10.1007/s00371-022-02518-x},
  pages     = {4019 -- 4033},
  year      = {2022},
  abstract  = {Fast style transfer methods have recently gained popularity in art-related applications as they make a generalized real-time stylization of images practicable. However, they are mostly limited to one-shot stylizations concerning the interactive adjustment of style elements. In particular, the expressive control over stroke sizes or stroke orientations remains an open challenge. To this end, we propose a novel stroke-adjustable fast style transfer network that enables simultaneous control over the stroke size and intensity, and allows a wider range of expressive editing than current approaches by utilizing the scale-variance of convolutional neural networks. Furthermore, we introduce a network-agnostic approach for style-element editing by applying reversible input transformations that can adjust strokes in the stylized output. At this, stroke orientations can be adjusted, and warping-based effects can be applied to stylistic elements, such as swirls or waves. To demonstrate the real-world applicability of our approach, we present StyleTune, a mobile app for interactive editing of neural style transfers at multiple levels of control. Our app allows stroke adjustments on a global and local level. It furthermore implements an on-device patch-based upsampling step that enables users to achieve results with high output fidelity and resolutions of more than 20 megapixels. Our approach allows users to art-direct their creations and achieve results that are not possible with current style transfer applications.},
  language  = {en}
}
@article{RosinLaiMouldetal.2022,
  author    = {Rosin, Paul L. and Lai, Yu-Kun and Mould, David and Yi, Ran and Berger, Itamar and Doyle, Lars and Lee, Seungyong and Li, Chuan and Liu, Yong-Jin and Semmo, Amir and Shamir, Ariel and Son, Minjung and Winnem{\"o}ller, Holger},
  title     = {NPRportrait 1.0: A three-level benchmark for non-photorealistic rendering of portraits},
  series = {Computational visual media},
  volume    = {8},
  journal   = {Computational visual media},
  number    = {3},
  publisher = {Springer Nature},
  address   = {London},
  issn      = {2096-0433},
  doi       = {10.1007/s41095-021-0255-3},
  pages     = {445 -- 465},
  year      = {2022},
  abstract  = {Recently, there has been an upsurge of activity in image-based non-photorealistic rendering (NPR), and in particular portrait image stylisation, due to the advent of neural style transfer (NST). However, the state of performance evaluation in this field is poor, especially compared to the norms in the computer vision and machine learning communities. Unfortunately, the task of evaluating image stylisation is thus far not well defined, since it involves subjective, perceptual, and aesthetic aspects. To make progress towards a solution, this paper proposes a new structured, three-level, benchmark dataset for the evaluation of stylised portrait images. Rigorous criteria were used for its construction, and its consistency was validated by user studies. Moreover, a new methodology has been developed for evaluating portrait stylisation algorithms, which makes use of the different benchmark levels as well as annotations provided by user studies regarding the characteristics of the faces. We perform evaluation for a wide variety of image stylisation methods (both portrait-specific and general purpose, and also both traditional NPR approaches and NST) using the new benchmark dataset.},
  language  = {en}
}