# Interactive Multi-level Stroke Control for Neural Style Transfer

arXiv: https://arxiv.org/abs/2106.13787 · HTML: https://mlsanity.com/paper/2106.13787/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 30 references that are in the map.
- First posted on arXiv 25 Jun 2021.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [A Learned Representation For Artistic Style](https://mlsanity.com/paper/1610.07629/) (2016)
- [State of the Art on Neural Rendering](https://mlsanity.com/paper/2004.03805/) (2020)
- [Neural Style Transfer: A Review](https://mlsanity.com/paper/1705.04058/) (2017)
- [Controlling Perceptual Factors in Neural Style Transfer](https://mlsanity.com/paper/1611.07865/) (2016)
- [Multi-style Generative Network for Real-time Transfer](https://mlsanity.com/paper/1703.06953/) (2017)
- [Attention-aware Multi-stroke Style Transfer](https://mlsanity.com/paper/1901.05127/) (2019)
- [Stroke Controllable Fast Style Transfer with Adaptive Receptive Fields](https://mlsanity.com/paper/1802.07101/) (2018)
- [Adjustable Real-time Style Transfer](https://mlsanity.com/paper/1811.08560/) (2018)
- [AI in the media and creative industries](https://mlsanity.com/paper/1905.04175/) (2019)

## Built on by (most cited first, 1 of 1)

- [Interactive Style Transfer: All is Your Palette](https://mlsanity.com/paper/2203.13470/) (2022)

## Abstract (arXiv)

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 enables control over stroke size and intensity and allows a larger range of edits than current approaches. For additional level-of-control, we propose a network agnostic method for stroke-orientation adjustment by utilizing the rotation-variance of CNNs. To achieve high output fidelity, we further add a patch-based style transfer method that enables users to obtain output resolutions of more than 20 Megapixel. Our approach empowers users to create many novel results that are not possible with current mobile neural style transfer apps.