1 code implementation • 31 Jul 2023 • Martin Büßemeyer, Max Reimann, Benito Buchheim, Amir Semmo, Jürgen Döllner, Matthias Trapp
We present a novel method for the interactive control of geometric abstraction and texture in artistic images.
1 code implementation • 2 Jan 2023 • Sumit Shekhar, Max Reimann, Moritz Hilscher, Amir Semmo, Jürgen Döllner, Matthias Trapp
For stylization tasks, however, consistency control is an essential requirement as a certain amount of flickering adds to the artistic look and feel.
2 code implementations • 29 Jul 2022 • Winfried Lötzsch, Max Reimann, Martin Büssemeyer, Amir Semmo, Jürgen Döllner, Matthias Trapp
Image-based artistic rendering can synthesize a variety of expressive styles using algorithmic image filtering.
1 code implementation • The Visual Computer 2022 • Max Reimann, Benito Buchheim, Amir Semmo, Jürgen Döllner, Matthias Trapp
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.
no code implementations • 9 Mar 2022 • Sumit Shekhar, Max Reimann, Amir Semmo, Sebastian Pasewaldt, Jürgen Döllner, Matthias Trapp
For videos captured in the wild, we perform a user study to demonstrate the preference for our method in comparison to state-of-the-art approaches.
no code implementations • 25 Jun 2021 • Max Reimann, Benito Buchheim, Amir Semmo, Jürgen Döllner, Matthias Trapp
We present StyleTune, a mobile app for interactive multi-level control of neural style transfers that facilitates creative adjustments of style elements and enables high output fidelity.
no code implementations • 1 Sep 2020 • Paul L. Rosin, Yu-Kun Lai, David Mould, Ran Yi, Itamar Berger, Lars Doyle, Seungyong Lee, Chuan Li, Yong-Jin Liu, Amir Semmo, Ariel Shamir, Minjung Son, Holger Winnemoller
Despite the recent upsurge of activity in image-based non-photorealistic rendering (NPR), and in particular portrait image stylisation, due to the advent of neural style transfer, the state of performance evaluation in this field is limited, especially compared to the norms in the computer vision and machine learning communities.