Search Results for author: Amir Semmo

Found 7 papers, 4 papers with code

Interactive Control over Temporal Consistency while Stylizing Video Streams

1 code implementation2 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.

Image Stylization Video Stabilization +1

Controlling strokes in fast neural style transfer using content transforms

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.

Style Transfer

Low-light Image and Video Enhancement via Selective Manipulation of Chromaticity

no code implementations9 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.

Video Enhancement

Interactive Multi-level Stroke Control for Neural Style Transfer

no code implementations25 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.

Style Transfer

NPRportrait 1.0: A Three-Level Benchmark for Non-Photorealistic Rendering of Portraits

no code implementations1 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.

Style Transfer

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