  In recent years we have witnessed tremendous progress in unpaired
image-to-image translation methods, propelled by the emergence of DNNs and
adversarial training strategies. However, most existing methods focus on
transfer of style and appearance, rather than on shape translation. The latter
task is challenging, due to its intricate non-local nature, which calls for
additional supervision. We mitigate this by descending the deep layers of a
pre-trained network, where the deep features contain more semantics, and
applying the translation from and between these deep features. Specifically, we
leverage VGG, which is a classification network, pre-trained with large-scale
semantic supervision. Our translation is performed in a cascaded,
deep-to-shallow, fashion, along the deep feature hierarchy: we first translate
between the deepest layers that encode the higher-level semantic content of the
image, proceeding to translate the shallower layers, conditioned on the deeper
ones. We show that our method is able to translate between different domains,
which exhibit significantly different shapes. We evaluate our method both
qualitatively and quantitatively and compare it to state-of-the-art
image-to-image translation methods. Our code and trained models will be made
available.
