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- W2979581729 abstract "We present a novel variational generative adversarial network (VGAN) based on Wasserstein loss to learn a latent representation from a face image that is invariant to identity but preserves head-pose information. This facilitates synthesis of a realistic face image with the same head pose as a given input image, but with a different identity. One application of this network is in privacy-sensitive scenarios; after identity replacement in an image, utility, such as head pose, can still be recovered. Extensive experimental validation on synthetic and real human-face image datasets performed under 3 threat scenarios confirms the ability of the proposed network to preserve head pose of the input image, mask the input identity, and synthesize a good-quality realistic face image of a desired identity. We also show that our network can be used to perform pose-preserving identity morphing and identity-preserving pose morphing. The proposed method improves over a recent state-of-the-art method in terms of quantitative metrics as well as synthesized image quality." @default.
- W2979581729 created "2019-10-18" @default.
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- W2979581729 date "2019-10-01" @default.
- W2979581729 modified "2023-10-16" @default.
- W2979581729 title "Vae/Wgan-Based Image Representation Learning For Pose-Preserving Seamless Identity Replacement In Facial Images" @default.
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- W2979581729 doi "https://doi.org/10.1109/mlsp.2019.8918926" @default.
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