Texture Attribute Synthesis and Transfer Using Feed-Forward CNNs

摘要

We present a novel technique for texture synthesis and style transfer based on convolutional neural networks (CNNs). Our method learns feed-forward image generators that correspond to specification of styles and textures in terms of high-level describable attributes such as 'striped', 'dotted', or 'veined'. Two key conceptual advantages over template-based approaches are that attributes can be analyzed and activated individually, while a template image necessarily represents a simultaneous specification of many attributes, and that attributes can combine aspects of many texture templates allowing flexibility in the generation process. Once the attribute-wise networks are trained, applications to texture synthesis and style transfer are fast, allowing for real-time video processing.