  Unsupervised image-to-image translation is the task of translating an image
from one domain to another in the absence of any paired training examples and
tends to be more applicable to practical applications. Nevertheless, the
extension of such synthesis from image-to-image to video-to-video is not
trivial especially when capturing spatio-temporal structures in videos. The
difficulty originates from the aspect that not only the visual appearance in
each frame but also motion between consecutive frames should be realistic and
consistent across transformation. This motivates us to explore both appearance
structure and temporal continuity in video synthesis. In this paper, we present
a new Motion-guided Cycle GAN, dubbed as Mocycle-GAN, that novelly integrates
motion estimation into unpaired video translator. Technically, Mocycle-GAN
capitalizes on three types of constrains: adversarial constraint discriminating
between synthetic and real frame, cycle consistency encouraging an inverse
translation on both frame and motion, and motion translation validating the
transfer of motion between consecutive frames. Extensive experiments are
conducted on video-to-labels and labels-to-video translation, and superior
results are reported when comparing to state-of-the-art methods. More
remarkably, we qualitatively demonstrate our Mocycle-GAN for both
flower-to-flower and ambient condition transfer.
