Boutros, Fadi ; Klemt, Marcel ; Fang, Meiling ; Kuijper, Arjan ; Damer, Naser (2023)
ExFaceGAN: Exploring Identity Directions in GAN’s Learned Latent Space for Synthetic Identity Generation.
International Joint Conference on Biometrics 2023. Ljubljana, Slovenia (25.-28.9.2023)
doi: 10.1109/IJCB57857.2023.10449036
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Deep generative models have recently presented impressive results in generating realistic face images of random synthetic identities. To generate multiple samples of a certain synthetic identity, previous works proposed to disentangle the latent space of GANs by incorporating additional supervision or regularization, enabling the manipulation of certain attributes. Others proposed to disentangle specific factors in unconditional pretrained GANs latent spaces to control their output, which also requires supervision by attribute classifiers. Moreover, these attributes are entangled in GAN’s latent space, making it difficult to manipulate them without affecting the identity information. We propose in this work a framework, ExFaceGAN, to disentangle identity information in pretrained GANs latent spaces, enabling the generation of multiple samples of any synthetic identity. Given a reference latent code of any synthetic image and latent space of pretrained GAN, our ExFaceGAN learns an identity directional boundary that disentangles the latent space into two sub-spaces, with latent codes of samples that are either identity similar or dissimilar to a reference image. By sampling from each side of the boundary, our ExFaceGAN can generate multiple samples of synthetic identity without the need for designing a dedicated architecture or supervision from attribute classifiers. We demonstrate the generalizability and effectiveness of ExFaceGAN by integrating it into learned latent spaces of three SOTA GAN approaches. As an example of the practical benefit of our ExFaceGAN, we empirically prove that data generated by ExFaceGAN can be successfully used to train face recognition models (https://github.com/fdbtrs/ExFaceGAN).
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2023 |
| Autor(en): | Boutros, Fadi ; Klemt, Marcel ; Fang, Meiling ; Kuijper, Arjan ; Damer, Naser |
| Art des Eintrags: | Bibliographie |
| Titel: | ExFaceGAN: Exploring Identity Directions in GAN’s Learned Latent Space for Synthetic Identity Generation |
| Sprache: | Englisch |
| Publikationsjahr: | 29 September 2023 |
| Verlag: | IEEE |
| Buchtitel: | 2023 IEEE International Joint Conference on Biometrics (IJCB) |
| Veranstaltungstitel: | International Joint Conference on Biometrics 2023 |
| Veranstaltungsort: | Ljubljana, Slovenia |
| Veranstaltungsdatum: | 25.-28.9.2023 |
| DOI: | 10.1109/IJCB57857.2023.10449036 |
| Kurzbeschreibung (Abstract): | Deep generative models have recently presented impressive results in generating realistic face images of random synthetic identities. To generate multiple samples of a certain synthetic identity, previous works proposed to disentangle the latent space of GANs by incorporating additional supervision or regularization, enabling the manipulation of certain attributes. Others proposed to disentangle specific factors in unconditional pretrained GANs latent spaces to control their output, which also requires supervision by attribute classifiers. Moreover, these attributes are entangled in GAN’s latent space, making it difficult to manipulate them without affecting the identity information. We propose in this work a framework, ExFaceGAN, to disentangle identity information in pretrained GANs latent spaces, enabling the generation of multiple samples of any synthetic identity. Given a reference latent code of any synthetic image and latent space of pretrained GAN, our ExFaceGAN learns an identity directional boundary that disentangles the latent space into two sub-spaces, with latent codes of samples that are either identity similar or dissimilar to a reference image. By sampling from each side of the boundary, our ExFaceGAN can generate multiple samples of synthetic identity without the need for designing a dedicated architecture or supervision from attribute classifiers. We demonstrate the generalizability and effectiveness of ExFaceGAN by integrating it into learned latent spaces of three SOTA GAN approaches. As an example of the practical benefit of our ExFaceGAN, we empirically prove that data generated by ExFaceGAN can be successfully used to train face recognition models (https://github.com/fdbtrs/ExFaceGAN). |
| Freie Schlagworte: | Biometrics, Face recognition, Machine learning, Generative Adversarial Networks (GAN), Deep learning |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme 20 Fachbereich Informatik > Mathematisches und angewandtes Visual Computing |
| Hinterlegungsdatum: | 12 Apr 2024 10:26 |
| Letzte Änderung: | 17 Dez 2024 10:49 |
| PPN: | 524687102 |
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