  Generative Adversarial Networks have shown impressive results for the task of
object translation, including face-to-face translation. A key component behind
the success of recent approaches is the self-consistency loss, which encourages
a network to recover the original input image when the output generated for a
desired attribute is itself passed through the same network, but with the
target attribute inverted. While the self-consistency loss yields
photo-realistic results, it can be shown that the input and target domains,
supposed to be close, differ substantially. This is empirically found by
observing that a network recovers the input image even if attributes other than
the inversion of the original goal are set as target. This stops one combining
networks for different tasks, or using a network to do progressive forward
passes. In this paper, we show empirical evidence of this effect, and propose a
new loss to bridge the gap between the distributions of the input and target
domains. This "triple consistency loss", aims to minimise the distance between
the outputs generated by the network for different routes to the target,
independent of any intermediate steps. To show this is effective, we
incorporate the triple consistency loss into the training of a new
landmark-guided face to face synthesis, where, contrary to previous works, the
generated images can simultaneously undergo a large transformation in both
expression and pose. To the best of our knowledge, we are the first to tackle
the problem of mismatching distributions in self-domain synthesis, and to
propose "in-the-wild" landmark-guided synthesis. Code will be available at
https://github.com/ESanchezLozano/GANnotation
