Abstract: A camera located behind a partially transparent display allows for a participant in a video conference to appear to be looking directly at other participants, creating a natural eye-to-eye setting similar to an in-person meeting. However, the camera picks up moiré patterns and other distortion from the display. A machine learning model is trained to predict the interference from a frame of video on the display. The predicted interference is then subtracted from a corresponding video frame from the camera, resulting in a clean image. The clean image is then used in place of the camera's video frame in output video, resulting in high quality video useful for a video conference.

Abstract: A camera located behind a partially transparent display allows for a participant in a video conference to appear to be looking directly at other participants, creating a natural eye-to-eye setting similar to an in-person meeting. However, the camera picks up moiré patterns and other distortion from the display. A machine learning model is trained to predict the interference from a frame of video on the display. The predicted interference is then subtracted from a corresponding video frame from the camera, resulting in a clean image. The clean image is then used in place of the camera's video frame in output video, resulting in high quality video useful for a video conference.