Face Image Animation with Adversarial Learning and Motion Transfer

Significant advances have been made in facial image animation from a single image. Nonetheless, generating convincing facial feature movements remains a complex challenge in computer graphics. The purpose of this study is to develop an efficient and effective approach for transferring motion from a source video to a single facial image by governing the position and expression of the face in the video to generate a new video imitating the source image. Compared to prior methods that focus solely on manipulating facial expressions, this model has been trained to distinguish the moving foreground from the background image and to create motions such as facial rotation and translation as well as small local motions such as gaze shift. The pro-posed technique uses generative adversarial networks GANs with a motion transfer model. The network forecasts photo-realistic video frames for a given target image using synthetic input in renderings from a parametric face model. The authenticity in this postprocessing conversion is attained by precise image manipulation. Thorough adversarial training is used to produce greater accuracy in this postprocessing conver-sion. Although more improvements to face landmark identification on videos and face super-resolution techniques have been made to improve the results, the pro-posed technique can provide more coherent videos with improved visual quality, resulting in more aligned landmark sequences for training. In addition, experiments indicate that we obtain superior results compared to those obtained by the state-of-the-art image-driven technique with PSNR 30.74 and SSIM 0.9