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.
翻译:神经风格传输是深层学习研究的一个众所周知的分支,有许多有趣的作品和两个主要缺陷。 实地的大部分工程很难被非专家用户使用,需要大量硬件资源。 在这项工作中,我们提出了解决这两个问题的办法。 我们将神经风格传输应用到实时视频(每秒25个以上),它能够运行在移动设备上。 我们还调查了实现时间一致性的工作,并提出了微调、已经经过培训的模型的想法,以便实现稳定的视频。 此外,我们还分析了共同的深神经网络结构对移动设备在现有层和过滤器数量方面性能的影响。 在试验部分,我们介绍了我们有关iOS设备的工作结果,并讨论了目前安卓装置中存在的问题以及未来的可能性。 最后,我们介绍了iPhone 11 Pro 和iPhone 6 上测试的性能质化和定量结果。 介绍的工作被纳入了苹果的Appre 。