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Local Facial Attribute Transfer through Inpainting

  • The term “attribute transfer” refers to the tasks of altering images in such a way, that the semantic interpretation of a given input image is shifted towards an intended direction, which is quantified by semantic attributes. Prominent example applications are photo realistic changes of facial features and expressions, like changing the hair color, adding a smile, enlarging the nose or alteringThe term “attribute transfer” refers to the tasks of altering images in such a way, that the semantic interpretation of a given input image is shifted towards an intended direction, which is quantified by semantic attributes. Prominent example applications are photo realistic changes of facial features and expressions, like changing the hair color, adding a smile, enlarging the nose or altering the entire context of a scene, like transforming a summer landscape into a winter panorama. Recent advances in attribute transfer are mostly based on generative deep neural networks, using various techniques to manipulate images in the latent space of the generator. In this paper, we present a novel method for the common sub-task of local attribute transfers, where only parts of a face have to be altered in order to achieve semantic changes (e.g. removing a mustache). In contrast to previous methods, where such local changes have been implemented by generating new (global) images, we propose to formulate local attribute transfers as an inpainting problem. Removing and regenerating only parts of images, our “Attribute Transfer Inpainting Generative Adversarial Network” (ATI-GAN) is able to utilize local context information to focus on the attributes while keeping the background unmodified resulting in visually sound results.show moreshow less

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Metadaten
Document Type:Conference Proceeding
Conference Type:Konferenzartikel
Zitierlink: https://opus.hs-offenburg.de/5282
Bibliografische Angaben
Title (English):Local Facial Attribute Transfer through Inpainting
Conference:International Conference on Pattern Recognition : ICPR (25. : Jan. 10 2021 to Jan. 15 2021 : Milan, Italy)
Author:Ricard Durall Lopez, Franz-Josef Pfreundt, Janis KeuperStaff MemberORCiDGND
Year of Publication:2021
Publisher:IEEE
First Page:95
Last Page:102
Parent Title (English):Proceedings of ICPR 2020 : 25th International Conference on Pattern Recognition : Milan, 10 – 15 January 2021
ISBN:978-1-7281-8808-9 (elektronisch)
ISBN:978-1-7281-8809-6 (Print on Demand)
ISSN:1051-4651
DOI:https://doi.org/10.1109/ICPR48806.2021.9412878
URL:https://www.computer.org/csdl/proceedings-article/icpr/2021/09412878/1tmhKsYHJCM
Language:English
Inhaltliche Informationen
Institutes:Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019)
Forschung / IMLA - Institute for Machine Learning and Analytics
Institutes:Bibliografie
Tag:face recognition; generative adversarial networks; hair; image color analysis; neural networks; nose; semantics
Formale Angaben
Open Access: Closed Access 
Licence (German):License LogoUrheberrechtlich geschützt