  Unpaired exemplar-based image-to-image (UEI2I) translation aims to translate
a source image to a target image domain with the style of a target image
exemplar, without ground-truth input-translation pairs. Existing UEI2I methods
represent style using one vector per image or rely on semantic supervision to
define one style vector per object. Here, in contrast, we propose to represent
style as a dense feature map, allowing for a finer-grained transfer to the
source image without requiring any external semantic information. We then rely
on perceptual and adversarial losses to disentangle our dense style and content
representations. To stylize the source content with the exemplar style, we
extract unsupervised cross-domain semantic correspondences and warp the
exemplar style to the source content. We demonstrate the effectiveness of our
method on four datasets using standard metrics together with a localized style
metric we propose, which measures style similarity in a class-wise manner. Our
results show that the translations produced by our approach are more diverse,
preserve the source content better, and are closer to the exemplars when
compared to the state-of-the-art methods. Project page:
https://github.com/IVRL/dsi2i
