@article{ShekharReimannMayeretal.2021,
  author    = {Shekhar, Sumit and Reimann, Max and Mayer, Maximilian and Semmo, Amir and Pasewaldt, Sebastian and D{\"o}llner, J{\"u}rgen and Trapp, Matthias},
  title     = {Interactive photo editing on smartphones via intrinsic decomposition},
  series = {Computer graphics forum : journal of the European Association for Computer Graphics},
  volume    = {40},
  journal   = {Computer graphics forum : journal of the European Association for Computer Graphics},
  publisher = {Blackwell},
  address   = {Oxford},
  issn      = {0167-7055},
  doi       = {10.1111/cgf.142650},
  pages     = {497 -- 510},
  year      = {2021},
  abstract  = {Intrinsic decomposition refers to the problem of estimating scene characteristics, such as albedo and shading, when one view or multiple views of a scene are provided. The inverse problem setting, where multiple unknowns are solved given a single known pixel-value, is highly under-constrained. When provided with correlating image and depth data, intrinsic scene decomposition can be facilitated using depth-based priors, which nowadays is easy to acquire with high-end smartphones by utilizing their depth sensors. In this work, we present a system for intrinsic decomposition of RGB-D images on smartphones and the algorithmic as well as design choices therein. Unlike state-of-the-art methods that assume only diffuse reflectance, we consider both diffuse and specular pixels. For this purpose, we present a novel specularity extraction algorithm based on a multi-scale intensity decomposition and chroma inpainting. At this, the diffuse component is further decomposed into albedo and shading components. We use an inertial proximal algorithm for non-convex optimization (iPiano) to ensure albedo sparsity. Our GPU-based visual processing is implemented on iOS via the Metal API and enables interactive performance on an iPhone 11 Pro. Further, a qualitative evaluation shows that we are able to obtain high-quality outputs. Furthermore, our proposed approach for specularity removal outperforms state-of-the-art approaches for real-world images, while our albedo and shading layer decomposition is faster than the prior work at a comparable output quality. Manifold applications such as recoloring, retexturing, relighting, appearance editing, and stylization are shown, each using the intrinsic layers obtained with our method and/or the corresponding depth data.},
  language  = {en}
}
@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}
}
@article{ReimannKlingbeilPasewaldtetal.2019,
  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     = {Locally controllable neural style transfer on mobile devices},
  series = {The Visual Computer},
  volume    = {35},
  journal   = {The Visual Computer},
  number    = {11},
  publisher = {Springer},
  address   = {New York},
  issn      = {0178-2789},
  doi       = {10.1007/s00371-019-01654-1},
  pages     = {1531 -- 1547},
  year      = {2019},
  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. 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.},
  language  = {en}
}
@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}
}
@book{HerbstMaschlerNiephausetal.2015,
  author    = {Herbst, Eva-Maria and Maschler, Fabian and Niephaus, Fabio and Reimann, Max and Steier, Julia and Felgentreff, Tim and Lincke, Jens and Taeumel, Marcel and Hirschfeld, Robert and Witt, Carsten},
  title     = {ecoControl},
  number    = {93},
  publisher = {Universit{\"a}tsverlag Potsdam},
  address   = {Potsdam},
  isbn      = {978-3-86956-318-3},
  issn      = {1613-5652},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-72147},
  publisher      = {Universit{\"a}t Potsdam},
  pages     = {viii, 142},
  year      = {2015},
  abstract  = {Eine dezentrale Energieversorgung ist ein erster Schritt in Richtung Energiewende. Dabei werden auch in Mehrfamilienh{\"a}usern vermehrt verschiedene Strom- und W{\"a}rmeerzeuger eingesetzt. Besonders in Deutschland kommen in diesem Zusammenhang Blockheizkraftwerke immer h{\"a}ufiger zum Einsatz, weil sie Gas sehr effizient in Strom und W{\"a}rme umwandeln k{\"o}nnen. Außerdem erm{\"o}glichen sie, im Zusammenspiel mit anderen Energiesystemen wie beispielsweise Photovoltaik-Anlagen, eine kontinuierliche und dezentrale Energieversorgung. Bei dem Betrieb von unterschiedlichen Energiesystemen ist es w{\"u}nschenswert, dass die Systeme aufeinander abgestimmt arbeiten. Allerdings ist es bisher schwierig, heterogene Energiesysteme effizient miteinander zu betreiben. Dadurch bleiben Einsparungspotentiale ungenutzt. Eine zentrale Steuerung kann deshalb die Effizienz des Gesamtsystems verbessern. Mit ecoControl stellen wir einen erweiterbaren Prototypen vor, der die Kooperation von Energiesystemen optimiert und Umweltfaktoren miteinbezieht. Dazu stellt die Software eine einheitliche Bedienungsoberfl{\"a}che zur Konfiguration aller Systeme zur Verf{\"u}gung. Außerdem bietet sie die M{\"o}glichkeit, Optimierungsalgorithmen mit Hilfe einer Programmierschnittstelle zu entwickeln, zu testen und auszuf{\"u}hren. Innerhalb solcher Algorithmen k{\"o}nnen von ecoControl bereitgestellte Vorhersagen genutzt werden. Diese Vorhersagen basieren auf dem individuellen Verhalten von jedem Energiesystem, Wettervorhersagen und auf Prognosen des Energieverbrauchs. Mithilfe einer Simulation k{\"o}nnen Techniker unterschiedliche Konfigurationen und Optimierungen sofort ausprobieren, ohne diese {\"u}ber einen langen Zeitraum an realen Ger{\"a}ten testen zu m{\"u}ssen. ecoControl hilft dar{\"u}ber hinaus auch Hausverwaltungen und Vermietern bei der Verwaltung und Analyse der Energiekosten. Wir haben anhand von Fallbeispielen gezeigt, dass Optimierungsalgorithmen, welche die Nutzung von W{\"a}rmespeichern verbessern, die Effizienz des Gesamtsystems erheblich verbessern k{\"o}nnen. Schließlich kommen wir zu dem Schluss, dass ecoControl in einem n{\"a}chsten Schritt unter echten Bedingungen getestet werden muss, sobald eine geeignete Hardwarekomponente verf{\"u}gbar ist. {\"U}ber diese Schnittstelle werden die Messwerte an ecoControl gesendet und Steuersignale an die Ger{\"a}te weitergeleitet.},
  language  = {de}
}