An Adaptive-Guidance GAN for Accurate Face Reenactment

Face reenactment has been widely used in face editing, augmentation and animation. However, it is still challenging to generate photo-realistic target face with accurate pose or expression as reference face, meanwhile retain the identity as the source face. To achieve this goal, we propose an Adaptive-Guidance Generative Adversarial Network (AD-GAN) for accurate face reenactment. Unlike previous methods that control GANs by either directly employing a simple set of vectors or sparse representations (e.g., facial landmarks or boundaries), which ignore the correspondence between reference and source faces, thus leading to inaccurate reenactment or artifacts on target faces. We devise a Correlation Module (CM) that can adaptively establish dense correspondence between a 3D face model as the conditions and the latent features from sources to formulate an indicator map for implementing explicit control of target faces. Besides, the Texture Module (TM) and Guiding Blocks (GB) in generator can restore the facial appearance distorted by expression or pose changes, and progressively guide the generation process. Extensive experiments demonstrate the superiority of our AD-GAN in generating photo-realistic and accurately controllable images.