Anzeige
Erschienen in:
2017 | OriginalPaper | Buchkapitel
DeepStyleCam: A Real-Time Style Transfer App on iOS
verfasst von : Ryosuke Tanno, Shin Matsuo, Wataru Shimoda, Keiji Yanai
Erschienen in: MultiMedia Modeling
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Abstract
In this demo, we present a very fast CNN-based style transfer system running on normal iPhones. The proposed app can transfer multiple pre-trained styles to the video stream captured from the built-in camera of an iPhone around 140ms (7fps). We extended the network proposed as a real-time neural style transfer network by Johnson et al. [1] so that the network can learn multiple styles at the same time. In addition, we modified the CNN network so that the amount of computation is reduced one tenth compared to the original network. The very fast mobile implementation of the app are based on our paper [2] which describes several new ideas to implement CNN on mobile devices efficiently. Figure 1 shows an example usage of DeepStyleCam which is running on an iPhone SE.