  Most image-to-image translation methods focus on learning mappings across
domains with the assumption that images share content (e.g., pose) but have
their own domain-specific information known as style. When conditioned on a
target image, such methods aim to extract the style of the target and combine
it with the content of the source image. In this work, we consider the scenario
where the target image has a very low resolution. More specifically, our
approach aims at transferring fine details from a high resolution (HR) source
image to fit a coarse, low resolution (LR) image representation of the target.
We therefore generate HR images that share features from both HR and LR inputs.
This differs from previous methods that focus on translating a given image
style into a target content, our translation approach being able to
simultaneously imitate the style and merge the structural information of the LR
target. Our approach relies on training the generative model to produce HR
target images that both 1) share distinctive information of the associated
source image; 2) correctly match the LR target image when downscaled. We
validate our method on the CelebA-HQ and AFHQ datasets by demonstrating
improvements in terms of visual quality, diversity and coverage. Qualitative
and quantitative results show that when dealing with intra-domain image
translation, our method generates more realistic samples compared to
state-of-the-art methods such as Stargan-v2
