  We introduce a method for egocentric videoconferencing that enables
hands-free video calls, for instance by people wearing smart glasses or other
mixed-reality devices. Videoconferencing portrays valuable non-verbal
communication and face expression cues, but usually requires a front-facing
camera. Using a frontal camera in a hands-free setting when a person is on the
move is impractical. Even holding a mobile phone camera in the front of the
face while sitting for a long duration is not convenient. To overcome these
issues, we propose a low-cost wearable egocentric camera setup that can be
integrated into smart glasses. Our goal is to mimic a classical video call, and
therefore, we transform the egocentric perspective of this camera into a front
facing video. To this end, we employ a conditional generative adversarial
neural network that learns a transition from the highly distorted egocentric
views to frontal views common in videoconferencing. Our approach learns to
transfer expression details directly from the egocentric view without using a
complex intermediate parametric expressions model, as it is used by related
face reenactment methods. We successfully handle subtle expressions, not easily
captured by parametric blendshape-based solutions, e.g., tongue movement, eye
movements, eye blinking, strong expressions and depth varying movements. To get
control over the rigid head movements in the target view, we condition the
generator on synthetic renderings of a moving neutral face. This allows us to
synthesis results at different head poses. Our technique produces temporally
smooth video-realistic renderings in real-time using a video-to-video
translation network in conjunction with a temporal discriminator. We
demonstrate the improved capabilities of our technique by comparing against
related state-of-the art approaches.
