Deep generative image priors for semantic face manipulation. (July 2023)
- Record Type:
- Journal Article
- Title:
- Deep generative image priors for semantic face manipulation. (July 2023)
- Main Title:
- Deep generative image priors for semantic face manipulation
- Authors:
- Hou, Xianxu
Shen, Linlin
Ming, Zhong
Qiu, Guoping - Abstract:
- Highlights: We propose a novel attribute-guided latent space optimization approach to control various facial attributes in the latent space of GANs. We propose a semantic-aware encoder for GAN inversion, which can not only faithfully reconstruct the input images, but also ensure that the inverted codes are semantically meaningful. We demonstrate the usefulness of synthesized face images for augmenting training data to improve weight initialization and accelerate the learning process for deep networks, leading to state-of-the-art performance for face attribute prediction. Abstract: Previous works on generative adversarial networks (GANs) mainly focus on how to synthesize high-fidelity images. In this paper, we present a framework to leverage the knowledge learned by GANs for semantic face manipulation. In particular, we propose to control the semantics of synthesized faces by adapting the latent codes with an attribute prediction model. Moreover, in order to achieve a more accurate estimation of different facial attributes, we propose to pretrain the attribute prediction model by inverting the synthesized face images back to the GAN latent space. As a result, our method explicitly considers the semantics encoded in the latent space of a pretrained GAN and is able to faithfully edit various attributes like eyeglasses, smiling, bald, age, mustache and gender for high-resolution face images. Extensive experiments show that our method has superior performance compared to state ofHighlights: We propose a novel attribute-guided latent space optimization approach to control various facial attributes in the latent space of GANs. We propose a semantic-aware encoder for GAN inversion, which can not only faithfully reconstruct the input images, but also ensure that the inverted codes are semantically meaningful. We demonstrate the usefulness of synthesized face images for augmenting training data to improve weight initialization and accelerate the learning process for deep networks, leading to state-of-the-art performance for face attribute prediction. Abstract: Previous works on generative adversarial networks (GANs) mainly focus on how to synthesize high-fidelity images. In this paper, we present a framework to leverage the knowledge learned by GANs for semantic face manipulation. In particular, we propose to control the semantics of synthesized faces by adapting the latent codes with an attribute prediction model. Moreover, in order to achieve a more accurate estimation of different facial attributes, we propose to pretrain the attribute prediction model by inverting the synthesized face images back to the GAN latent space. As a result, our method explicitly considers the semantics encoded in the latent space of a pretrained GAN and is able to faithfully edit various attributes like eyeglasses, smiling, bald, age, mustache and gender for high-resolution face images. Extensive experiments show that our method has superior performance compared to state of the art for both face attribute prediction and semantic face manipulation. … (more)
- Is Part Of:
- Pattern recognition. Volume 139(2023)
- Journal:
- Pattern recognition
- Issue:
- Volume 139(2023)
- Issue Display:
- Volume 139, Issue 2023 (2023)
- Year:
- 2023
- Volume:
- 139
- Issue:
- 2023
- Issue Sort Value:
- 2023-0139-2023-0000
- Page Start:
- Page End:
- Publication Date:
- 2023-07
- Subjects:
- GANs -- Face attribute prediction -- Semantic face manipulation
Pattern perception -- Periodicals
Perception des structures -- Périodiques
Patroonherkenning
006.4 - Journal URLs:
- http://www.sciencedirect.com/science/journal/00313203 ↗
http://www.sciencedirect.com/ ↗ - DOI:
- 10.1016/j.patcog.2023.109477 ↗
- Languages:
- English
- ISSNs:
- 0031-3203
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 26855.xml