  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 have the
attribute designated by given perceptual description; these multi-label
semantic attributes are treated as condition variables to generate wallpaper
images. The generated wallpaper images can be converted to those with
well-known artist styles using CycleGAN. Finally, using the aesthetic
evaluation method, the generated wallpaper images are quantitatively measured.
The experimental results demonstrate that the proposed method can generate
wallpaper textures conforming to human aesthetics and have artistic
characteristics.
