Corpus ID: 235652401

Interactive Multi-level Stroke Control for Neural Style Transfer

  title={Interactive Multi-level Stroke Control for Neural Style Transfer},
  author={Max Reimann and Benito Buchheim and Amir Semmo and Jurgen Dollner and 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. In contrast to current mobile neural style transfer apps, StyleTune supports users to adjust both the size and orientation of style elements, such as brushstrokes and texture patches, on a global as well as local level. To this end, we propose a novel stroke-adaptive feed-forward style transfer network, that… Expand

Figures from this paper


Stroke Controllable Fast Style Transfer with Adaptive Receptive Fields
This paper presents a stroke controllable style transfer network that incorporates different stroke sizes into one single model and demonstrates that with almost the same number of parameters as the previous Fast Style Transfer algorithm, this network can transfer an artistic style in a stroke controlled manner. Expand
Multi-style Generative Network for Real-time Transfer
MSG-Net is the first to achieve real-time brush-size control in a purely feed-forward manner for style transfer and is compatible with most existing techniques including content-style interpolation, color-preserving, spatial control and brush stroke size control. Expand
Attention-Aware Multi-Stroke Style Transfer
This paper proposes to assemble self-attention mechanism into a style-agnostic reconstruction autoencoder framework, from which the attention map of a content image can be derived, and develops an attention-aware multi-stroke style transfer model. Expand
Adjustable Real-time Style Transfer
A novel method is proposed which allows adjustment of crucial hyper-parameters, after the training and in real-time, through a set of manually adjustable parameters, which enable the user to modify the synthesized outputs from the same pair of style/content images, in search of a favorite stylized image. Expand
Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization
This paper presents a simple yet effective approach that for the first time enables arbitrary style transfer in real-time, comparable to the fastest existing approach, without the restriction to a pre-defined set of styles. Expand
Neural style transfer: a paradigm shift for image-based artistic rendering?
This meta paper categorize style transfers within the taxonomy of IB-AR, then proposes a semiotic structure to derive a technical research agenda for NSTs with respect to the grand challenges of NPAR. Expand
Direction-aware neural style transfer with texture enhancement
It is found that direction, that is, the orientation of each painting stroke, can capture the soul of image style preferably and thus generates much more natural and vivid stylizations. Expand
MaeSTrO: A Mobile App for Style Transfer Orchestration Using Neural Networks
MaeSTrO is presented, 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 and first user tests indicate different levels of satisfaction for the implemented techniques and interaction design. Expand
Enhancing neural style transfer using patch-based synthesis
This work takes advantage of neural techniques to provide adequate stylization at the global level and uses their output as a prior for subsequent patch-based synthesis at the detail level to achieve compelling stylization quality even for high-resolution imagery. Expand
Controlling Perceptual Factors in Neural Style Transfer
The existing Neural Style Transfer method is extended to introduce control over spatial location, colour information and across spatial scale, enabling the combination of style information from multiple sources to generate new, perceptually appealing styles from existing ones. Expand