  Neural style transfer is a well-known branch of deep learning research, with
many interesting works and two major drawbacks. Most of the works in the field
are hard to use by non-expert users and substantial hardware resources are
required. In this work, we present a solution to both of these problems. We
have applied neural style transfer to real-time video (over 25 frames per
second), which is capable of running on mobile devices. We also investigate the
works on achieving temporal coherence and present the idea of fine-tuning,
already trained models, to achieve stable video. What is more, we also analyze
the impact of the common deep neural network architecture on the performance of
mobile devices with regard to number of layers and filters present. In the
experiment section we present the results of our work with respect to the iOS
devices and discuss the problems present in current Android devices as well as
future possibilities. At the end we present the qualitative results of
stylization and quantitative results of performance tested on the iPhone 11 Pro
and iPhone 6s. The presented work is incorporated in Kunster - AR Art Video
Maker application available in the Apple's App Store.
