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Neural style transfer is a well-known branch of deep learning research, withmany interesting works and two major drawbacks. Most of the works in the fieldare hard to use by non-expert users and substantial hardware resources arerequired. In this work, we present a solution to both of these problems. Wehave applied neural style transfer to real-time video (over 25 frames persecond), which is capable of running on mobile devices. We also investigate theworks on achieving temporal coherence and present the idea of fine-tuning,already trained models, to achieve stable video. What is more, we also analyzethe impact of the common deep neural network architecture on the performance ofmobile devices with regard to number of layers and filters present. In theexperiment section we present the results of our work with respect to the iOSdevices and discuss the problems present in current Android devices as well asfuture possibilities. At the end we present the qualitative results ofstylization and quantitative results of performance tested on the iPhone 11 Proand iPhone 6s. The presented work is incorporated in Kunster - AR Art VideoMaker application available in the Apple's App Store.

Kunster -- AR Art Video Maker -- Real time video neural style transfer on mobile devices