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This paper addresses a major flaw of the cycle consistency loss when used topreserve the input appearance in the face-to-face synthesis domain. Inparticular, we show that the images generated by a network trained using thisloss conceal a noise that hinders their use for further tasks. To overcome thislimitation, we propose a ''recurrent cycle consistency loss" which fordifferent sequences of target attributes minimises the distance between theoutput images, independent of any intermediate step. We empirically validatenot only that our loss enables the re-use of generated images, but that it alsoimproves their quality. In addition, we propose the very first network thatcovers the task of unconstrained landmark-guided face-to-face synthesis.Contrary to previous works, our proposed approach enables the transfer of aparticular set of input features to a large span of poses and expressions,whereby the target landmarks become the ground-truth points. We then evaluatethe consistency of our proposed approach to synthesise faces at the targetlandmarks. To the best of our knowledge, we are the first to propose a loss toovercome the limitation of the cycle consistency loss, and the first to proposean ''in-the-wild'' landmark guided synthesis approach. Code and models for thispaper can be found in https://github.com/ESanchezLozano/GANnotation

A recurrent cycle consistency loss for progressive face-to-face synthesis