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Wallpaper Texture Generation and Style Transfer Based on Multi-label Semantics

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posted on 27.05.2021, 11:38 by Y Gao, X Feng, T Zhang, E Rigall, Huiyu Zhou, L Qi, J Dong
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.

History

Citation

IEEE Transactions on Circuits and Systems for Video Technology, doi: 10.1109/TCSVT.2021.3078560.

Author affiliation

School of Informatics

Version

AM (Accepted Manuscript)

Published in

IEEE Transactions on Circuits and Systems for Video Technology

Publisher

Institute of Electrical and Electronics Engineers

issn

1051-8215

Acceptance date

29/04/2021

Copyright date

2021

Available date

27/05/2021

Language

en

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