Video Synthesis of Human Upper Body with Realistic Face This paper presents a generative adversarial learningbased 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 personu0027s facial action units and poses into the target personu0027s facial landmarks, then combine the normalized upper body keypoints and generated facial landmarks with spatiotemporal smoothing to generate the corresponding target videou0027s image. Experimental results demonstrated the effectiveness of our method.

Neural stylepreserving visual dubbing Dubbing is a technique for translating video content from one language to another. However, stateoftheart visual dubbing techniques directly copy facial expressions from source to target actors without considering identityspecific idiosyncrasies such as a unique type of smile. We present a stylepreserving visual dubbing approach from single video inputs, which maintains the signature style of target actors when modifying facial expressions, including mouth motions, to match foreign languages. At the heart of our approach is the concept of motion style, in particular for facial expressions, i.e., the personspecific expression change that is yet another essential factor beyond visual accuracy in face editing applications. Our method is based on a recurrent generative adversarial network that captures the spatiotemporal coactivation of facial expressions, and enables generating and modifying the facial expressions of the target actor while preserving their style. We train our model with unsynchronized source and target videos in an unsupervised manner using cycleconsistency and mouth expression losses, and synthesize photorealistic video frames using a layered neural face renderer. Our approach generates temporally coherent results, and handles dynamic backgrounds. Our results show that our dubbing approach maintains the idiosyncratic style of the target actor better than previous approaches, even for widely differing source and target actors.