Style attention based global-local aware GAN for personalized facial caricature generation - Zhao, Xiuzhi; Chen, Wenting; Xie, Weicheng; Shen, Linlin (Frontiers in Neuroscience, 2023)
Caricature is an exaggerated pictorial representation of a person, which is widely used in entertainment and political media. Recently, GAN-based methods achieved automatic caricature generation through transferring caricature style and performing shape exaggeration simultaneously. However, the caricature synthesized by these methods cannot perfectly reflect the characteristics of the subject, whose shape exaggeration are not reasonable and requires facial landmarks of caricature. In addition, the existing methods always produce the bad cases in caricature style due to the simpleness of their style transfer method. In this paper, we propose a Style Attention based Global-local Aware GAN to apply the characteristics of a subject to generate personalized facial caricature. To integrate the facial characteristics of a subject, we introduce a landmark-based warp controller for personalized shape exaggeration, which employs the facial landmarks as control points to warp image according to its facial features, without requirement of the facial landmarks of caricature. To fuse the facial feature with caricature style appropriately, we introduce a style-attention module, which adopts an attention mechanism, instead of the simple Adaptive Instance Normalization (AdaIN) for style transfer. To reduce the bad cases and increase the quality of generated caricatures, we propose a multi-scale discriminator to both globally and locally discriminate the synthesized and real caricature, which improves the whole structure and realistic details of the synthesized caricature. Experimental results on two publicly available datasets, the WebCaricature dataset and CaVINet dataset, validate the effectiveness of our proposed method and suggest that our proposed method achieves better performance than the existing methods. Source code is to be released for the public access.