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Now showing 1 - 3 of 3
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    Reducing Affective Responses to Surgical Images through Color Manipulation and Stylization
    (ACM, 2018) Besançon, Lonni; Semmo, Amir; Biau, David; Frachet, Bruno; Pineau, Virginie; Sariali, El Hadi; Taouachi, Rabah; Isenberg, Tobias; Dragicevic, Pierre; Aydın, Tunç and Sýkora, Daniel
    We present the first empirical study on using color manipulation and stylization to make surgery images more palatable. While aversion to such images is natural, it limits many people's ability to satisfy their curiosity, educate themselves, and make informed decisions. We selected a diverse set of image processing techniques, and tested them both on surgeons and lay people. While many artistic methods were found unusable by surgeons, edge-preserving image smoothing gave good results both in terms of preserving information (as judged by surgeons) and reducing repulsiveness (as judged by lay people). Color manipulation turned out to be not as effective.
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    MNPR: A Framework for Real-Time Expressive Non-Photorealistic Rendering of 3D Computer Graphics
    (ACM, 2018) Montesdeoca, Santiago E.; Seah, Hock Soon; Semmo, Amir; Bénard, Pierre; Vergne, Romain; Thollot, Joëlle; Benvenuti, Davide; Aydın, Tunç and Sýkora, Daniel
    We propose a framework for expressive non-photorealistic rendering of 3D computer graphics: MNPR. Our work focuses on enabling stylization pipelines with a wide range of control, thereby covering the interaction spectrum with real-time feedback. In addition, we introduce control semantics that allow crossstylistic art-direction, which is demonstrated through our implemented watercolor, oil and charcoal stylizations. Our generalized control semantics and their style-specific mappings are designed to be extrapolated to other styles, by adhering to the same control scheme. We then share our implementation details by breaking down our framework and elaborating on its inner workings. Finally, we evaluate the usefulness of each level of control through a user study involving 20 experienced artists and engineers in the industry, who have collectively spent over 245 hours using our system. MNPR is implemented in Autodesk Maya and open-sourced through this publication, to facilitate adoption by artists and further development by the expressive research and development community.
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    Approaches for Local Artistic Control of Mobile Neural Style Transfer
    (ACM, 2018) Reimann, Max; Klingbeil, Mandy; Pasewaldt, Sebastian; Semmo, Amir; Döllner, Jürgen; Trapp, Matthias; Aydın, Tunç and Sýkora, Daniel
    This 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.