Multi-style art image generation from sketch

Binghui Zheng; Yonghua Zhu; Zhuo Bi; Wenjun Zhang

doi:10.1117/12.2680767

23 May 2023 Multi-style art image generation from sketch

Binghui Zheng, Yonghua Zhu, Zhuo Bi, Wenjun Zhang

Proceedings Volume 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023); 126451G (2023) https://doi.org/10.1117/12.2680767
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 2023, Hangzhou, China

Abstract

Although generative models have made great progress in art image generation, few of them pay attention to sketch-based art image generation. Generating art images from sketch is a challenging problem suffering from two main issues: (1) how to constrain the generation of art images with sparse sketch features and (2) how to fully utilize the style information of a reference image without being influenced by their content features. To tackle these, we propose a GAN-based method for art image generation given the sketch and reference style. Specifically, a style feature enhancement module and a sketch-adaptive normalization module are constructed to enable the disentanglement of the content information from the reference style image. Experiments and comparisons demonstrate the superiority of our model over current generative models in sketch-based art image generation.

Citation Download Citation

Binghui Zheng, Yonghua Zhu, Zhuo Bi, and Wenjun Zhang "Multi-style art image generation from sketch", Proc. SPIE 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 126451G (23 May 2023); https://doi.org/10.1117/12.2680767

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