High-resolution face swapping via latent semantics disentanglement

We present a novel high-resolution face swapping method using the inherent prior knowledge of a pre-trained GAN model. Although previous research can leverage generative priors to produce high-resolution results, their quality can suffer from the entangled semantics of the latent space. We explicitl...

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Bibliographic Details
Main Authors: XU, Yangyang, DENG, Bailin, WANG, Junle, JING, Yanqing, PAN, Jia, HE, Shengfeng
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2022
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Online Access:https://ink.library.smu.edu.sg/sis_research/8532
https://ink.library.smu.edu.sg/context/sis_research/article/9535/viewcontent/Xu_High_Resolution_Face_Swapping_via_Latent_Semantics_Disentanglement_CVPR_2022_paper.pdf
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Institution: Singapore Management University
Language: English

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