论文标题
Kunster- AR艺术视频制造商 - 移动设备上的实时视频神经风格转移
Kunster -- AR Art Video Maker -- Real time video neural style transfer on mobile devices
论文作者
论文摘要
神经风格转移是深度学习研究的著名分支，其中有许多有趣的作品和两个主要缺点。非专家用户很难使用该领域的大多数作品，需要大量的硬件资源。在这项工作中，我们为这两个问题提供了解决方案。我们已经将神经样式转移应用于实时视频（每秒25帧），该视频能够在移动设备上运行。我们还研究了有关实现时间连贯性的作品，并提出了已经训练有素的模型的微调概念，以实现稳定的视频。更重要的是，我们还分析了共同的深神经网络体系结构对目前的层和过滤器数量的移动设备性能的影响。在实验部分中，我们介绍了有关iOS设备的工作结果，并讨论了当前的Android设备中存在的问题以及未来的可能性。最后，我们介绍了在iPhone 11 Pro和iPhone 6s上测试性能的定性结果和定量结果。介绍的作品包含在Kunster -AR Art Video Maker应用程序中，可在Apple的App Store中提供。
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.
