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• W3175559777 abstract "Face sketch synthesis has made significant progress with the development of deep neural networks in these years. The delicate depiction of sketch portraits facilitates a wide range of applications like digital entertainment and law enforcement. However, accurate and realistic face sketch generation is still a challenging task due to the illumination variations and complex backgrounds in the real scenes. To tackle these challenges, we propose a novel Semantic-Driven Generative Adversarial Network (SDGAN) which embeds global structure-level style injection and local class-level knowledge re-weighting. Specifically, we conduct facial saliency detection on the input face photos to provide overall facial texture structure, which could be used as a global type of prior information. In addition, we exploit face parsing layouts as the semantic-level spatial prior to enforce globally structural style injection in the generator of SDGAN. Furthermore, to enhance the realistic effect of the details, we propose a novel Adaptive Re-weighting Loss (ARLoss) which dedicates to balance the contributions of different semantic classes. Experimentally, our extensive experiments on CUFS and CUFSF datasets show that our proposed algorithm achieves state-of-the-art performance." @default.
• W3175559777 created "2021-07-05" @default.
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• W3175559777 date "2021-06-29" @default.
• W3175559777 modified "2023-10-16" @default.
• W3175559777 title "Face Sketch Synthesis via Semantic-Driven Generative Adversarial Network" @default.
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• W3175559777 doi "https://doi.org/10.48550/arxiv.2106.15121" @default.
• W3175559777 hasPublicationYear "2021" @default.
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