1 citations · 2 across the 9 of their papers we have counts for
4 papers · 1 filter
ManiFest: Manifold Deformation for Few-shot Image Translation
Fabio Pizzati, Jean-François Lalonde, Raoul de Charette
Most image-to-image translation methods require a large number of training images, which restricts their applicability. We instead propose ManiFest: a framework for few-shot image…
Leveraging Local Domains for Image-to-Image Translation
Anthony Dell'Eva, Fabio Pizzati, Massimo Bertozzi +1
Image-to-image (i2i) networks struggle to capture local changes because they do not affect the global scene structure. For example, translating from highway scenes to offroad, i2i…
Physics-informed Guided Disentanglement in Generative Networks
Fabio Pizzati, Pietro Cerri, Raoul de Charette
Image-to-image translation (i2i) networks suffer from entanglement effects in presence of physics-related phenomena in target domain (such as occlusions, fog, etc), lowering altoge…
CoMoGAN: continuous model-guided image-to-image translation
Fabio Pizzati, Pietro Cerri, Raoul de Charette
CoMoGAN is a continuous GAN relying on the unsupervised reorganization of the target data on a functional manifold. To that matter, we introduce a new Functional Instance Normaliza…