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- W4321636706 abstract "Face sketch-photo synthesis aims at generating a facial photo conditioned on a given photo. It has attracted wide attention in computer vision and has been widely applied in law enforcement and entertainment. However, precisely synthesizing high-quality face photos is still challenging due to the missing color information in face sketches. To alleviate the problem, we propose an attribute and Semantic constrained Generative Adversarial Networks (ASGAN), which introduces face attribute and semantic constraints to improve the quality and accuracy of synthesized photos. Specifically, We first annotate the key attributes in the dataset and form a 14-dimensional attribute vector for each face. Then, the attribute features and semantic features obtained by face parsing are fused. Finally, the generator takes the fused constrain features and sketches as input and constructs two-stream encoders to synthesize high-quality photos. Extensive experimental results demonstrate that our method can significantly outperform state-of-the-art methods. It can synthe-size higher-quality face photos while maintaining the identity, attributes, and structure. Meanwhile, it can alleviate the problem of background and skin color synthesis errors." @default.
- W4321636706 created "2023-02-24" @default.
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- W4321636706 date "2022-11-01" @default.
- W4321636706 modified "2023-09-26" @default.
- W4321636706 title "Attributes and Semantic Constrained GAN for Face Sketch-Photo Synthesis" @default.
- W4321636706 doi "https://doi.org/10.1109/wcsp55476.2022.10039309" @default.
- W4321636706 hasPublicationYear "2022" @default.
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