Interactive Multi-level Stroke Control for Neural Style Transfer
This addresses the need for more creative control in mobile style transfer apps, though it is incremental in building on existing neural style transfer methods.
The paper tackles the problem of limited user control in mobile neural style transfer by introducing StyleTune, an app that enables interactive multi-level adjustments of style elements like stroke size and orientation, achieving output resolutions over 20 Megapixel.
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.