图片-神经风格的转换和编辑与coreML

S. Pasewaldt, Amir Semmo, Mandy Klingbeil, J. Döllner
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引用次数: 2

摘要

这项工作介绍了Pictory的设计和实现方面的进展,Pictory是一款使用CoreML和Metal api进行艺术神经风格转移和交互式图像编辑的iOS应用程序。Pictory结合了神经风格迁移的优点,例如,在全球范围内的高度抽象,以及gpu加速的最先进的基于图像的局部艺术渲染的交互性。因此,用户可以使用两阶段方法创建高分辨率、抽象的再现。首先,使用预训练的卷积神经网络对照片进行变换,以获得中间的程式化表示。其次,基于图像的艺术渲染技术(例如,水彩,油画或卡通过滤)被用来进一步使图像风格化。因此,过滤了由样式转移引入的细尺度纹理噪声,并提供了在运行时单独调整样式化效果的交互手段。基于定性和定量的用户研究,Pictory经过重新设计和优化,通过提供有效且易于理解的工具来促进多层次控制的图像编辑,以支持临时用户以及移动艺术家。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pictory - neural style transfer and editing with coreML
This work presents advances in the design and implementation of Pictory, an iOS app for artistic neural style transfer and interactive image editing using the CoreML and Metal APIs. Pictory combines the benefits of neural style transfer, e.g., high degree of abstraction on a global scale, with the interactivity of GPU-accelerated state-of-the-art image-based artistic rendering on a local scale. Thereby, the user is empowered to create high-resolution, abstracted renditions in a two-stage approach. First, a photo is transformed using a pre-trained convolutional neural network to obtain an intermediate stylized representation. Second, image-based artistic rendering techniques (e.g., watercolor, oil paint or toon filtering) are used to further stylize the image. Thereby, fine-scale texture noise---introduced by the style transfer---is filtered and interactive means are provided to individually adjust the stylization effects at run-time. Based on qualitative and quantitative user studies, Pictory has been redesigned and optimized to support casual users as well as mobile artists by providing effective, yet easy to understand, tools to facilitate image editing at multiple levels of control.
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