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Domain Agnostic Image-to-image Translation using Low-Resolution Conditioning by Abid, Mohamed; Afrasiyabi, Arman; Hedhli, Ihsen; Lalonde, Jean-François; Gagné, Christian is a scholarly article available to read on EtoBox.

What is Domain Agnostic Image-to-image Translation using Low-Resolution Conditioning about?

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.

Author

Abid, Mohamed; Afrasiyabi, Arman; Hedhli, Ihsen; Lalonde, Jean-François; Gagné, Christian

Published

2023

Language

EN