In a typical video conferencing setup, it is hard to maintain eye contact during a call since it requires looking into the camera rather than the display. We propose an eye contact correction model that restores the eye contact regardless of the relative position of the camera and display. Unlike previous solutions, our model redirects the gaze from an arbitrary direction to the center without requiring a redirection angle or camera display user geometry as inputs. We use a deep convolutional neural network that inputs a monocular image and produces a vector field and a brightness map to correct the gaze. We train this model in a bi-directional way on a large set of synthetically generated photorealistic images with perfect labels. The learned model is a robust eye contact corrector which also predicts the input gaze implicitly at no additional cost. Our system is primarily designed to improve the quality of video conferencing experience. Therefore, we use a set of control mechanisms to prevent creepy results and to ensure a smooth and natural video conferencing experience. The entire eye contact correction system runs end-to-end in real-time on a commodity CPU and does not require any dedicated hardware, making our solution feasible for a variety of devices.
Effective communication using current video conferencing systems is severely hindered by the lack of eye contact caused by the disparity between the locations of the subject and the camera. While this problem has been partially solved for high-end expensive video conferencing systems, it has not been convincingly solved for consumer-level setups. We present a gaze correction approach based on a single Kinect sensor that preserves both the integrity and expressiveness of the face as well as the fidelity of the scene as a whole, producing nearly artifact-free imagery. Our method is suitable for mainstream home video conferencing: it uses inexpensive consumer hardware, achieves real-time performance and requires just a simple and short setup. Our approach is based on the observation that for our application it is sufficient to synthesize only the corrected face. Thus we render a gaze-corrected 3D model of the scene and, with the aid of a face tracker, transfer the gaze-corrected facial portion in a seamless manner onto the original image.