  This paper addresses a major flaw of the cycle consistency loss when used to
preserve the input appearance in the face-to-face synthesis domain. In
particular, we show that the images generated by a network trained using this
loss conceal a noise that hinders their use for further tasks. To overcome this
limitation, we propose a ''recurrent cycle consistency loss" which for
different sequences of target attributes minimises the distance between the
output images, independent of any intermediate step. We empirically validate
not only that our loss enables the re-use of generated images, but that it also
improves their quality. In addition, we propose the very first network that
covers the task of unconstrained landmark-guided face-to-face synthesis.
Contrary to previous works, our proposed approach enables the transfer of a
particular set of input features to a large span of poses and expressions,
whereby the target landmarks become the ground-truth points. We then evaluate
the consistency of our proposed approach to synthesise faces at the target
landmarks. To the best of our knowledge, we are the first to propose a loss to
overcome the limitation of the cycle consistency loss, and the first to propose
an ''in-the-wild'' landmark guided synthesis approach. Code and models for this
paper can be found in https://github.com/ESanchezLozano/GANnotation
