FacialGAN: Style Transfer and Attribute Manipulation on Synthetic Faces
- Facial image manipulation is a generation task where the output face is shifted towards an intended target direction in terms of facial attribute and styles. Recent works have achieved great success in various editing techniques such as style transfer and attribute translation. However, current approaches are either focusing on pure style transfer, or on the translation of predefined sets ofFacial image manipulation is a generation task where the output face is shifted towards an intended target direction in terms of facial attribute and styles. Recent works have achieved great success in various editing techniques such as style transfer and attribute translation. However, current approaches are either focusing on pure style transfer, or on the translation of predefined sets of attributes with restricted interactivity. To address this issue, we propose FacialGAN, a novel framework enabling simultaneous rich style transfers and interactive facial attributes manipulation. While preserving the identity of a source image, we transfer the diverse styles of a target image to the source image. We then incorporate the geometry information of a segmentation mask to provide a fine-grained manipulation of facial attributes. Finally, a multi-objective learning strategy is introduced to optimize the loss of each specific tasks. Experiments on the CelebA-HQ dataset, with CelebAMask-HQ as semantic mask labels, show our model’s capacity in producing visually compelling results in style transfer, attribute manipulation, diversity and face verification. For reproducibility, we provide an interactive open-source tool to perform facial manipulations, and the Pytorch implementation of the model.…
Document Type: | Conference Proceeding |
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Conference Type: | Konferenzartikel |
Zitierlink: | https://opus.hs-offenburg.de/5287 | Bibliografische Angaben |
Title (English): | FacialGAN: Style Transfer and Attribute Manipulation on Synthetic Faces |
Conference: | British Machine Vision Conference (32. : 22nd - 25th November 2021 : Online) |
Author: | Ricard Durall Lopez, Jireh Jam, Dominik Strassel, Moi Hoon Yap, Janis KeuperStaff MemberORCiDGND |
Year of Publication: | 2021 |
Contributing Corporation: | British Machine Vision Association |
Page Number: | 14 |
First Page: | 1 |
Last Page: | 14 |
Parent Title (English): | [32nd British Machine Vision Conference] |
URL: | https://www.bmvc2021-virtualconference.com/conference/papers/paper_0325.html |
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: | attribute manipulation; face editing; gan; style transfer | Formale Angaben |
Open Access: | Open Access |
Licence (German): | Urheberrechtlich geschützt |
ArXiv Id: | http://arxiv.org/abs/2110.09425 |