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- W4385864364 abstract "Image deformation refers to deforming objects in images into a target shape or posture. Although point-based image deformation algorithms have made breakthroughs in performance and visual effects, they are limited to warping the current structure information in the source image. For example, while opening a closed mouth in a face deformation, the point-based algorithm cannot generate teeth, which causes the mouth to twist weirdly. Deep learning-based face editing models can generate new parts but cannot achieve fine pixel-level manipulation. To overcome these challenges, we propose a two-step strategy for face deformation. We first generate a high-resolution intermediate image by blending the source image and the specific part of the target image via our face blending generative adversarial network (FB-GAN). Then, we employ a state-of-the-art point-based geometric deformation method to deform the intermediate image with target face guidance. Extensive experiments show that the proposed FB-GAN can generate realistic and high-resolution results and demonstrates that the two-step face deformation strategy can be better applied to human face deformation." @default.
- W4385864364 created "2023-08-17" @default.
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- W4385864364 date "2023-01-01" @default.
- W4385864364 modified "2023-10-16" @default.
- W4385864364 title "Face Deformation Under Feature Transfer and Geometric Deformation" @default.
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- W4385864364 doi "https://doi.org/10.1109/lsp.2023.3305911" @default.
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