Hybridization of silhouette rendering and pen-and-ink illustration of non-photorealistic rendering technique for 3D object

This study proposes a hybrid of Non-photorealistic Rendering techniques. Nonphotorealistic Rendering (NPR) covers one part in computer graphics that caters towards generating many kinds of 2D digital art style from 3D data, for instance output that looks like painting and drawing. NPR includes the painterly, interpretative, expressive and artistic styles, among others. NPR research deal with different issues such as the stylization that are driven by human perception, the science and art that were brought together and being harmonized with techniques used. Some of approaches used in NPR were discussed such as cartoon rendering, watercolour painting, silhouette rendering, penand- ink illustration and so on. A plan for hybridization of NPR techniques is proposed between silhouette rendering techniques and pen-and-ink illustration for this study. The integration process of these rendering techniques takes on the lighting mapping and also the construction of colour region of the model in order to ensure the pen-and-ink illustration texture can be implemented into the object. The evaluation process is based on the visualization of the image from the hybridization process. Based on findings, the hybridization of NPR technique was able to create interesting results and considered as an alternative in producing new variety of visualization image in NPR

Facial expression transfer using generative adversarial network : a review

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