  Cross-domain image-to-image translation should satisfy two requirements: (1)
preserve the information that is common to both domains, and (2) generate
convincing images covering variations that appear in the target domain. This is
challenging, especially when there are no example translations available as
supervision. 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 provide
users with compelling translations of real scenes, because the translations do
not have high-fidelity constraints. In other words, current models are liable
to change details that should be preserved: while re-texturing a face, they may
alter the face's expression in an unpredictable way. In this paper, we
introduce the problem of high-fidelity image-to-image translation, and present
a method for solving it. Our main insight is that low-fidelity translations
typically escape a cycle-consistency penalty, because the back-translator
learns to compensate for the forward-translator's errors. We therefore
introduce an optimization technique that prevents the networks from
cooperating: simply train each network only when its input data is real. Prior
works, in comparison, train each network with a mix of real and generated data.
Experimental results show that our method accurately disentangles the factors
that separate the domains, and converges to semantics-preserving translations
that prior methods miss.
