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.
We revisit the well-known problem of gaze correction and present a solution based on supervised machine learning. At training time, our system observes pairs of images, where each pair contains the face of the same person with a fixed angular difference in gaze direction. It then learns to synthesize the second image of a pair from the first one. After learning, the system gets the ability to redirect the gaze of a previously unseen person by the same angular difference as in the training set. Unlike many previous solutions to gaze problem in videoconferencing, ours is purely monocular, i.e. it does not require any hardware apart from an in-built web-camera of a laptop. Being based on efficient machine learning predictors such as decision forests, the system is fast (runs in real-time on a single core of a modern laptop). In the paper, we demonstrate results on a variety of videoconferencing frames and evaluate the method quantitatively on the hold-out set of registered images. The supplementary video shows example sessions of our system at work. 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. A new algorithm is proposed for novel view generation in one-to-one teleconferencing applications. Given the video streams acquired by two cameras placed on either side of a computer monitor, the proposed algorithm synthesizes images from a virtual camera in arbitrary position (typically located within the monitor) to facilitate eye contact. Our technique is based on an improved, dynamic-programming, stereo algorithm for efficient novel-view generation. The two main contributions are: i) a new type of three-plane graph for dense-stereo dynamic-programming, that encourages correct occlusion labeling; ii) a compact geometric derivation for novel-view synthesis by direct projection of the minimum-cost surface. Furthermore, we present a novel algorithm for the temporal maintenance of a background model to enhance the rendering of occlusions and reduce temporal artefacts (flicker); and a cost aggregation algorithm that acts directly on our three-dimensional matching cost space. Examples are given that demonstrate the robustness of the new algorithm to spatial and temporal artefacts for long stereo video streams. These include demonstrations of synthesis of cyclopean views of extended conversational sequences. We further demonstrate synthesis from a freely translating virtual camera.  In video conferences, one user can either look at the remote image of the other captured by a video camera, or look at the camera that is capturing her him, but not two tasks at the same time. The lack of eye contact caused by this misalignment substantially reduces the effectiveness of communication with an unpleasant feeling of disconnectedness. We propose an approach to bring eye contact back while user looks at the remote image of the other by a novel system composed by a Time-of-Flight depth sensor and traditional stereo. The key success of this system is to faithfully recover scene's depth. In this 2.5D space, the controlling of the user's eye-gaze becomes relatively easier. To evaluate the performance of the system, we conducted two user studies. One focuses on subjects have been trained to be familiar to eye gaze displayed in images; another is blind evaluation that subjects have no prior knowledge about eye gaze. Both evaluations show that the system can bring desktop participants closer to each other.