Wallpaper Texture Generation and Style Transfer Based on Multi-label Semantics
Ying Gao, Xiaohan Feng, Tiange Zhang, Eric Rigall, Huiyu Zhou, Lin Qi,, Junyu Dong

TL;DR
This paper introduces a multi-label semantic approach combined with GANs for realistic wallpaper texture generation and style transfer, improving artistic quality and aesthetic alignment.
Contribution
It presents a novel framework using multi-label semantics and perceptual models for wallpaper texture synthesis and style transfer with CycleGAN integration.
Findings
Generated wallpapers match human aesthetic preferences.
The method produces textures with artistic characteristics.
Quantitative aesthetic evaluation confirms effectiveness.
Abstract
Textures contain a wealth of image information and are widely used in various fields such as computer graphics and computer vision. With the development of machine learning, the texture synthesis and generation have been greatly improved. As a very common element in everyday life, wallpapers contain a wealth of texture information, making it difficult to annotate with a simple single label. Moreover, wallpaper designers spend significant time to create different styles of wallpaper. For this purpose, this paper proposes to describe wallpaper texture images by using multi-label semantics. Based on these labels and generative adversarial networks, we present a framework for perception driven wallpaper texture generation and style transfer. In this framework, a perceptual model is trained to recognize whether the wallpapers produced by the generator network are sufficiently realistic and…
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Taxonomy
Methods*Communicated@Fast*How Do I Communicate to Expedia? · Batch Normalization · Residual Connection · Sigmoid Activation · HuMan(Expedia)||How do I get a human at Expedia? · GAN Least Squares Loss · Instance Normalization · PatchGAN · Cycle Consistency Loss · Residual Block
