Face Image Animation with Adversarial Learning and Motion Transfer

Dublin Core

Title

Face Image Animation with Adversarial Learning and Motion Transfer

Subject

Adversarial learning; face image super-resolution; image-to-video; motion trans-fer

Description

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 0.90

Creator

Karim, Abdulamir A.
Saleh, Suha Mohammed

Source

International Journal of Interactive Mobile Technologies (iJIM); Vol. 16 No. 10 (2022); pp. 109-121
1865-7923

Publisher

International Association of Online Engineering (IAOE), Vienna, Austria

Date

2022-05-24

Rights

Copyright (c) 2022 Haider Th.Salim Alrikabi; Abdulamir A. Karim, Suha Mohammed Saleh
https://creativecommons.org/licenses/by/4.0

Relation

Format

application/pdf

Language

eng

Type

info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article

Identifier

Citation

Abdulamir Karim A. and Suha Saleh Mohammed, Face Image Animation with Adversarial Learning and Motion Transfer, International Association of Online Engineering (IAOE), Vienna, Austria, 2022, accessed November 6, 2024, https://igi.indrastra.com/items/show/2253

Social Bookmarking