  This paper presents a generative adversarial learning-based human upper body
video synthesis approach to generate an upper body video of target person that
is consistent with the body motion, face expression, and pose of the person in
source video. We use upper body keypoints, facial action units and poses as
intermediate representations between source video and target video. Instead of
directly transferring the source video to the target video, we firstly map the
source person's facial action units and poses into the target person's facial
landmarks, then combine the normalized upper body keypoints and generated
facial landmarks with spatio-temporal smoothing to generate the corresponding
target video's image. Experimental results demonstrated the effectiveness of
our method.
