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• W4308236845 abstract "Image translation from human faces to anime ones brings a low-end, efficient way to create animation characters for animation industry. However, due to the significant inter-domain difference between anime images and human photos, existing image-to-image translation approaches cannot address this task well. To solve this dilemma, we propose HyProGAN, an exemplar-guided image-to-image translation model without paired data. The key contribution of HyPro-GAN is that it introduces a novel hybrid and progressive training strategy that expands the unidirectional translation between two domains into the bidirectional intra-domain and inter-domain translation. To enhance the consistency between input and output, we further propose a local masking loss to align the facial features between the human face and the generated anime face. Extensive experiments demonstrate the superiority of HyProGAN against state-of-the-art models." @default.
• W4308236845 created "2022-11-09" @default.
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• W4308236845 date "2022-10-16" @default.
• W4308236845 modified "2023-09-30" @default.
• W4308236845 title "Hyprogan: Breaking the Dimensional wall From Human to Anime" @default.
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• W4308236845 doi "https://doi.org/10.1109/icip46576.2022.9897973" @default.
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