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 Normalization layer and residual mechanism, which together disentangle image content from position on target manifold. We rely on naive physics-inspired models to guide the training while allowing private model/translations features. CoMoGAN can be used with any GAN backbone and allows new types of image translation, such as cyclic image translation like timelapse generation, or detached linear translation. On all datasets, it outperforms the literature. Our code is available at http://github.com/cv-rits/CoMoGAN .
@article{arxiv.2103.06879,
title = {CoMoGAN: continuous model-guided image-to-image translation},
author = {Fabio Pizzati and Pietro Cerri and Raoul de Charette},
journal= {arXiv preprint arXiv:2103.06879},
year = {2022}
}