  In this paper, we investigate the problem of unpaired video-to-video
translation. Given a video in the source domain, we aim to learn the
conditional distribution of the corresponding video in the target domain,
without seeing any pairs of corresponding videos. While significant progress
has been made in the unpaired translation of images, directly applying these
methods to an input video leads to low visual quality due to the additional
time dimension. In particular, previous methods suffer from semantic
inconsistency (i.e., semantic label flipping) and temporal flickering
artifacts. To alleviate these issues, we propose a new framework that is
composed of carefully-designed generators and discriminators, coupled with two
core objective functions: 1) content preserving loss and 2) temporal
consistency loss. Extensive qualitative and quantitative evaluations
demonstrate the superior performance of the proposed method against previous
approaches. We further apply our framework to a domain adaptation task and
achieve favorable results.
