Co-occurrence based texture synthesis  

作　　者：Anna Darzi Itai Lang Ashutosh Taklikar Hadar Averbuch-Elor Shai Avidan 

机构地区：[1]Tel Aviv University,Tel Aviv 6997801,Israel [2]Cornell-Tech,Cornell University,NYC,NY,10044,USA

出　　处：《Computational Visual Media》2022年第2期289-302,共14页计算可视媒体（英文版）

摘　　要：As image generation techniques mature,there is a growing interest in explainable representations that are easy to understand and intuitive to manipulate.In this work,we turn to co-occurrence statistics,which have long been used for texture analysis,to learn a controllable texture synthesis model.We propose a fully convolutional generative adversarial network,conditioned locally on co-occurrence statistics,to generate arbitrarily large images while having local,interpretable control over texture appearance.To encourage fidelity to the input condition,we introduce a novel differentiable co-occurrence loss that is integrated seamlessly into our framework in an end-to-end fashion.We demonstrate that our solution offers a stable,intuitive,and interpretable latent representation for texture synthesis,which can be used to generate smooth texture morphs between different textures.We further show an interactive texture tool that allows a user to adjust local characteristics of the synthesized texture by directly using the co-occurrence values.

关 键 词：CO-OCCURRENCE texture synthesis deep learning generative adversarial networks(GANs) 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]