There is high demand of realistic facial expression in current computer graphics and multimedia research. Realistic and accurate facial expression can guarantee the animated character to deliver the expression correctly. However, generating facial expression requires hard work, effort and time since high realism of facial expression need to be in details. There are some available methods in current research area such as face warping to the target, re-use the existing images and also models for generating facial image with certain attribute. Based on literature reviews, current trend for facial expression is using the deep learning method such as generative model like Generative Adversarial Network (GANs). Some of GANs that recently available are Conditional Generative Adversarial Network (cGANs), Double Encoder Conditional GAN (DECGAN), Conditional Difference Adversarial AutoEncoder (CDAAE), Geometry-Guided Generative Adversarial Network (G2GAN), and Geometry-Contrastive Generative Adversarial Network (GC-GAN). These methods actually helped in creating more realistic images, reaching out the realistic facial expression and good identity preservation. This paper aims to review available GANs, find out related features to these methods and also performance of these methods that are useful in facial expression transfer process

Generating facial animation has always been a challenge towards the graphical visualization area. Numerous efforts had been carried out in order to achieve high realism in facial animation. This paper surveys techniques applied in facial animation targeting towards realistic facial animation. We discuss the facial modeling techniques from different viewpoints; related geometric-based manipulation (that can be further categorized into interpolations, parameterization, muscle-based and pseudo–muscle-based model) and facial animation techniques involving speech-driven, image-based and data-captured. The paper will summarize and describe the related theories, strength and weaknesses for each technique.

Facial modeling has been an ongoing research for many years and still shows research trend due to its relevance to current technology. Many applications incorporate facial expression modeling with the help of facial tracking, facial animation and facial recognition. The existing performance of the modeling method faces the challenges to perform well due to many factors. Currently, the use of 2D images and videos as inputs for modeling process are gaining popularity. However, current technologies and development had extended the trends towards acquiring 3D human data. This paper provides an overview on variety of modeling techniques based on human facial model that can lead to future research.