Gaze correction is a type of video re-synthesis problem that trains to redirect a person’s eye gaze into camera by manipulating the eye area. It has many applications like video conferencing, movies, games and has a great future in medical fields such as to experiment with people having autism. Existing methods are incapable of gaze redirection of video using GAN. The suggested approach is based on the in-painting model to read from the face and fill the missed eye regions with new contents, reflecting corrected eye gaze in this paper. Both gaze estimation as well as gaze redirection have been implemented . The Hourglass model of CNN was used for gaze estimation and the Generative Adversarial Network(GAN) for video gaze redirection, in which two neural networks compete in a game to learn and produce new data with the same statistics as the training set. In addition, various losses were estimated such as discriminator, generator loss and perceptual loss in order to determine the accuracy of our model and evaluate the performance by adversarial divergence, reconstruction error and image quality measures. We demonstrate that the proposed method outperforms in terms of quality of the image and redirection precision in comprehensive tests.