CooGAN: A Memory-Efficient Framework for High-Resolution Facial Attribute Editing

Chen, Xuanhong; Ni, Bingbing; Liu, Naiyuan; Liu, Ziang; Jiang, Yiliu; Truong, Loc

doi:10.1007/978-3-030-58621-8_39

Cited by 12 publications

(9 citation statements)

References 21 publications

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“…This metric is used in many papers such as [19,50,91,100,119,120,144,152,174,198,206]. There are also some other versions of SSIM index such as MSSIM (mean SSIM) and MS-SSIM (multiscale SSIM).…”

Section: B Evaluation Metricsmentioning

confidence: 99%

A comprehensive survey on semantic facial attribute editing using generative adversarial networks

Nickabadi¹,

Fard²,

Farid³

et al. 2022

Preprint

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Section: B Evaluation Metricsmentioning

confidence: 99%

A comprehensive survey on semantic facial attribute editing using generative adversarial networks

Nickabadi¹,

Fard²,

Farid³

et al. 2022

Preprint

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“…These works are limited to low resolution images. Recently, Chen et al [12] proposed a pixel translation framework for high resolution facial image editing. Viazovetskyi et al [13] used generated high resolution images to train the pix2pixHD [14] for facial attribute manipulation.…”

Section: Related Workmentioning

confidence: 99%

Learning Non-Linear Disentangled Editing For Stylegan

Yao

Newson

Gousseau

et al. 2021

2021 IEEE International Conference on Image Processing (ICIP)

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EyeglassesGray Hair Age Hairline Original Slender Smiling Wavy Hair Makeup Sequential disentangled attribute manipulation. We show in this example how to achieve realistic, controllable, disentangled face editing. From the original image (center), we propose two opposite editing directions where only one attribute is manipulated at a time. To the right: 'slender', 'smiling', 'wavy hair' and 'makeup' and to the left: 'receding hairline', 'age', 'gray hair' and 'eyeglasses'. All results are obtained at resolution 1024 2 .

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“…Several studies investigate different possibilities to tackle high resolution images. CooGAN [7] proposed a patch-based local-global framework to process HR images in patches. Observing the great progress of generative networks in high quality image synthesis, Viazovetskyi et al [39] trained the pix2pixHD model [40] for single attribute editing with the synthetic images generated by StyleGAN2 [21].…”

Section: Related Workmentioning

confidence: 99%