Generative Adversarial Networks

Latent Space Manipulation of GANs for Seamless Image Compositing

Anna Fruehstueck, Ph.D., Computer Science

Apr 17, 17:30 - 18:30

B5 L5 R5220

Generative Adversarial Networks image synthesis texture synthesis

Generative Adversarial Networks (GANs) are a very successful method for high-quality image synthesis and are a powerful tool to generate realistic images by learning their visual properties from a dataset of exemplars. However, the controllability of the generator output still poses many challenges. In this thesis, we propose several methods for achieving larger and/or higher visual quality in GAN outputs by combining latent space manipulations with image compositing operations