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• W2905751765 abstract "Despite the significant success in image-to-image translation and latent representation based facial attribute editing and expression synthesis, the existing approaches still have limitations in the sharpness of details, distinct image translation and identity preservation. To address these issues, we propose a Texture Deformation Based GAN, namely TDB-GAN, to disentangle texture from original image and transfers domains based on the extracted texture. The approach utilizes the texture to transfer facial attributes and expressions without the consideration of the object pose. This leads to shaper details and more distinct visual effect of the synthesized faces. In addition, it brings the faster convergence during training. The effectiveness of the proposed method is validated through extensive ablation studies. We also evaluate our approach qualitatively and quantitatively on facial attribute and facial expression synthesis. The results on both the CelebA and RaFD datasets suggest that Texture Deformation Based GAN achieves better performance." @default.
• W2905751765 created "2019-01-01" @default.
• W2905751765 creator A5002593519 @default.
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• W2905751765 date "2018-12-24" @default.
• W2905751765 modified "2023-10-16" @default.
• W2905751765 title "Texture Deformation Based Generative Adversarial Networks for Face Editing" @default.
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• W2905751765 doi "https://doi.org/10.48550/arxiv.1812.09832" @default.
• W2905751765 hasPublicationYear "2018" @default.
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