DRB-GAN: A Dynamic ResBlock Generative Adversarial Network for Artistic Style Transfer

Wenju Xu, Chengjiang Long, Ruisheng Wang, Guanghui Wang; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 6383-6392

Abstract


In this work, we propose a Dynamic ResBlock Generative Adversarial Network (DRB-GAN) for artistic style transfer. The style code is modeled as the shared parameters for Dynamic ResBlocks connecting both the style encoding network and the style transfer network. In the style encoding network, a style class-aware attention mechanism is used to attend the style feature represent for generating the style codes. In the style transfer network, multiple Dynamic ResBlocks are designed to integrate the style code and the extracted CNN semantic feature and and then feed into the spatial window Layer-Instance Normalization (SW-LIN) decoder, which enables high-quality synthetic images with artistic style transfer. Moreover, the style collection conditional discriminator is designed to ensure our DRB-GAN model to equip with abilities for both arbitrary style transfer and collection style transfer during the training stage. No matter for arbitrary style transfer or collection style transfer, extensive experimental results strongly demonstrate that our proposed DRB-GAN beats state-of-the-art methods and exhibits its superior performance in terms of visual quality and efficiency.

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[bibtex]
@InProceedings{Xu_2021_ICCV, author = {Xu, Wenju and Long, Chengjiang and Wang, Ruisheng and Wang, Guanghui}, title = {DRB-GAN: A Dynamic ResBlock Generative Adversarial Network for Artistic Style Transfer}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {6383-6392} }