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Cross-domain image-to-image translation should satisfy two requirements: (1)preserve the information that is common to both domains, and (2) generateconvincing images covering variations that appear in the target domain. This ischallenging, especially when there are no example translations available assupervision. Adversarial cycle consistency was recently proposed as a solution,with beautiful and creative results, yielding much follow-up work. However,augmented reality applications cannot readily use such techniques to provideusers with compelling translations of real scenes, because the translations donot have high-fidelity constraints. In other words, current models are liableto change details that should be preserved: while re-texturing a face, they mayalter the face's expression in an unpredictable way. In this paper, weintroduce the problem of high-fidelity image-to-image translation, and presenta method for solving it. Our main insight is that low-fidelity translationstypically escape a cycle-consistency penalty, because the back-translatorlearns to compensate for the forward-translator's errors. We thereforeintroduce an optimization technique that prevents the networks fromcooperating: simply train each network only when its input data is real. Priorworks, in comparison, train each network with a mix of real and generated data.Experimental results show that our method accurately disentangles the factorsthat separate the domains, and converges to semantics-preserving translationsthat prior methods miss.

Image Disentanglement and Uncooperative Re-Entanglement for High-Fidelity Image-to-Image Translation