  Generally, image-to-image translation (i2i) methods aim at learning mappings
across domains with the assumption that the images used for translation share
content (e.g., pose) but have their own domain-specific information (a.k.a.
style). Conditioned on a target image, such methods extract the target style
and combine it with the source image content, keeping coherence between the
domains. In our proposal, we depart from this traditional view and instead
consider the scenario where the target domain is represented by a very
low-resolution (LR) image, proposing a domain-agnostic i2i method for
fine-grained problems, where the domains are related. More specifically, our
domain-agnostic approach aims at generating an image that combines visual
features from the source image with low-frequency information (e.g. pose,
color) of the LR target image. To do so, we present a novel approach that
relies on training the generative model to produce images that both share
distinctive information of the associated source image and 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.
Qualitative and quantitative results show that when dealing with intra-domain
image translation, our method generates realistic samples compared to
state-of-the-art methods such as StarGAN v2. Ablation studies also reveal that
our method is robust to changes in color, it can be applied to
out-of-distribution images, and it allows for manual control over the final
results.
