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Image-to-image translation based face photo de-meshing using GANs.

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Title: Image-to-image translation based face photo de-meshing using GANs.
Authors: Jabbar, Abdul1 (AUTHOR) jabbar@zju.edu.cn, Assam, Muhammad1 (AUTHOR) assam@zju.edu.cn, Arslan, Muhammad2 (AUTHOR) mhmd.arslan@outlook.com, Bukhsh, Madiha3 (AUTHOR) madiha@zju.edu.cn, Amin, Muhammad Shoib4 (AUTHOR) 52184501030@stu.ecnu.edu.cn, Ghadi, Yazeed Yasin5 (AUTHOR) yazeed.ghadi@aau.ac.ae, Innab, Nisreen6 (AUTHOR) ninnab@um.edu.sa, Alajmi, Masoud7 (AUTHOR) ms.alajmi@tu.edu.sa, Orken, Mamyrbayev8 (AUTHOR) morkenj@mail.ru, Indira, Salgozha9 (AUTHOR) indi_s@mail.ru, Alkahtan, Hend Khalid1,10 (AUTHOR) Hkalqahtani@pnu.edu.sa
Source: Computer Vision & Image Understanding. Oct2024, Vol. 247, pN.PAG-N.PAG. 1p.
Subjects: Generative adversarial networks, Performance theory
Abstract: Most of the existing face photo de-meshing methods have accomplished promising results; there are certain quality problems with these methods like the inpainted regions would appear blurry and unpleasant boundaries becoming visible. Such artifacts cause generated face photos unreal. Therefore, we propose an effective image-to-image translation framework called Face De-meshing Using Generative Adversarial Networks (De-mesh GANs). The De-mesh GANs is a two-stage model: (i) binary mask generating module, is a three convolution layers-based encoder–decoder network architecture that automatically generates a binary mask for the meshed region, and (ii) face photo de-meshing module, is a GANs-based network that eliminates the mesh mask and synthesizes the meshed area. An arrangement of careful losses (reconstruction loss, adversarial loss, and perceptual loss) is used to reassure the better quality of the de-mesh face photos. To facilitate the training of the proposed model, we have designed a dataset of clean/corrupted photo pairs using the CelebA dataset. Qualitative and quantitative evaluations of the De-mesh GANs on real-world corrupted face photo images show better performance than the previously proposed face photo de-meshing models. Furthermore, we also offer the ablation study for performance assessment of the additional network i.e., perceptual network. [ABSTRACT FROM AUTHOR]
Copyright of Computer Vision & Image Understanding is the property of Academic Press Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Image-to-image translation based face photo de-meshing using GANs.
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  Data: <searchLink fieldCode="AR" term="%22Jabbar%2C+Abdul%22">Jabbar, Abdul</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> jabbar@zju.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Assam%2C+Muhammad%22">Assam, Muhammad</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> assam@zju.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Arslan%2C+Muhammad%22">Arslan, Muhammad</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> mhmd.arslan@outlook.com</i><br /><searchLink fieldCode="AR" term="%22Bukhsh%2C+Madiha%22">Bukhsh, Madiha</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> madiha@zju.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Amin%2C+Muhammad+Shoib%22">Amin, Muhammad Shoib</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> 52184501030@stu.ecnu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Ghadi%2C+Yazeed+Yasin%22">Ghadi, Yazeed Yasin</searchLink><relatesTo>5</relatesTo> (AUTHOR)<i> yazeed.ghadi@aau.ac.ae</i><br /><searchLink fieldCode="AR" term="%22Innab%2C+Nisreen%22">Innab, Nisreen</searchLink><relatesTo>6</relatesTo> (AUTHOR)<i> ninnab@um.edu.sa</i><br /><searchLink fieldCode="AR" term="%22Alajmi%2C+Masoud%22">Alajmi, Masoud</searchLink><relatesTo>7</relatesTo> (AUTHOR)<i> ms.alajmi@tu.edu.sa</i><br /><searchLink fieldCode="AR" term="%22Orken%2C+Mamyrbayev%22">Orken, Mamyrbayev</searchLink><relatesTo>8</relatesTo> (AUTHOR)<i> morkenj@mail.ru</i><br /><searchLink fieldCode="AR" term="%22Indira%2C+Salgozha%22">Indira, Salgozha</searchLink><relatesTo>9</relatesTo> (AUTHOR)<i> indi_s@mail.ru</i><br /><searchLink fieldCode="AR" term="%22Alkahtan%2C+Hend+Khalid%22">Alkahtan, Hend Khalid</searchLink><relatesTo>1,10</relatesTo> (AUTHOR)<i> Hkalqahtani@pnu.edu.sa</i>
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  Data: <searchLink fieldCode="JN" term="%22Computer+Vision+%26+Image+Understanding%22">Computer Vision & Image Understanding</searchLink>. Oct2024, Vol. 247, pN.PAG-N.PAG. 1p.
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  Data: Most of the existing face photo de-meshing methods have accomplished promising results; there are certain quality problems with these methods like the inpainted regions would appear blurry and unpleasant boundaries becoming visible. Such artifacts cause generated face photos unreal. Therefore, we propose an effective image-to-image translation framework called Face De-meshing Using Generative Adversarial Networks (De-mesh GANs). The De-mesh GANs is a two-stage model: (i) binary mask generating module, is a three convolution layers-based encoder–decoder network architecture that automatically generates a binary mask for the meshed region, and (ii) face photo de-meshing module, is a GANs-based network that eliminates the mesh mask and synthesizes the meshed area. An arrangement of careful losses (reconstruction loss, adversarial loss, and perceptual loss) is used to reassure the better quality of the de-mesh face photos. To facilitate the training of the proposed model, we have designed a dataset of clean/corrupted photo pairs using the CelebA dataset. Qualitative and quantitative evaluations of the De-mesh GANs on real-world corrupted face photo images show better performance than the previously proposed face photo de-meshing models. Furthermore, we also offer the ablation study for performance assessment of the additional network i.e., perceptual network. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Computer Vision & Image Understanding is the property of Academic Press Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1016/j.cviu.2024.104080
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      – Code: eng
        Text: English
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        PageCount: 1
        StartPage: N.PAG
    Subjects:
      – SubjectFull: Generative adversarial networks
        Type: general
      – SubjectFull: Performance theory
        Type: general
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      – TitleFull: Image-to-image translation based face photo de-meshing using GANs.
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              Text: Oct2024
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