Ferla, Romano

Spreeuwers, Luuk

Zeinstra, Chris G.

English

Advancing the research in face applications islimited by proprietary databases and increasing data pro-tection regulations, synthetically generated databases mayprovide a solution. In this work the GANformer, a hybridgenerative image model, is explored for this application.While only trained for unconditioned face generation likemany other models, this works shows the potential of twouse cases. First, the unique implementation of the attentionis examined for the application of segmentation. Second,real labeled faces are reconstructed in latent space to findlatent directions describing disentangled attributes. Thisconcept is brought in practice by augmenting neutral tosmiling faces, but could be applied on other expressionsand attributes as well. This work can be use as basis asit opens up two directions for further research

University of Twente Research Information

Exploring the GANformer for Face Generation:Investigating the segmentation and smile augmentation potential

https://ris.utwente.nl/ws/files/296423503/proceedings_SITB2022.pdf

Exploring the GANformer for Face Generation:Investigating the segmentation and smile augmentation potential

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University of Twente Research Information