Art Directed Watercolor Shader for Non-photorealistic Rendering with a Focus on Reflections

In this research, I demonstrated that emulating painterly reflections is impossible using existing modeling, compositing and rendering software that does not provide programming capabilities. To obtain painterly reflections, we need to emulate three aspects of painterly reflections: (1) shape of reflections; (2) glossiness of reflections; and (3) colors of reflections. The first two turn out to be relatively easy. However, despite the perceived simplicity of color reproduction, the third one turned out to be hardest without developing our own proprietary tools. To demonstrate the difficulty, I have developed a shader using commercial rendering and shading software that does not provide explicit programming power. I assigned my shader as a surface material to 3D objects. Using my shader, I was able to create computer generated watercolor style renderings without reflections. My shader provide rendering effects such as diffuse, contours, specularity, shadow, and reflections. Although I can faithfully emulate non-reflected regions of given water-color paintings, I demonstrate that my shader cannot produce reflection colors that are faithful to colors of original reflections

Stroke-based Neural Painting and Stylization with Dynamically Predicted Painting Region

Stroke-based rendering aims to recreate an image with a set of strokes. Most existing methods render complex images using an uniform-block-dividing strategy, which leads to boundary inconsistency artifacts. To solve the problem, we propose Compositional Neural Painter, a novel stroke-based rendering framework which dynamically predicts the next painting region based on the current canvas, instead of dividing the image plane uniformly into painting regions. We start from an empty canvas and divide the painting process into several steps. At each step, a compositor network trained with a phasic RL strategy first predicts the next painting region, then a painter network trained with a WGAN discriminator predicts stroke parameters, and a stroke renderer paints the strokes onto the painting region of the current canvas. Moreover, we extend our method to stroke-based style transfer with a novel differentiable distance transform loss, which helps preserve the structure of the input image during stroke-based stylization. Extensive experiments show our model outperforms the existing models in both stroke-based neural painting and stroke-based stylization. Code is available at https://github.com/sjtuplayer/Compositional_Neural_PainterComment: ACM MM 202

Painterly rendering techniques: A state-of-the-art review of current approaches

In this publication we will look at the different methods presented over the past few decades which attempt to recreate digital paintings. While previous surveys concentrate on the broader subject of non-photorealistic rendering, the focus of this paper is firmly placed on painterly rendering techniques. We compare different methods used to produce different output painting styles such as abstract, colour pencil, watercolour, oriental, oil and pastel. Whereas some methods demand a high level of interaction using a skilled artist, others require simple parameters provided by a user with little or no artistic experience. Many methods attempt to provide more automation with the use of varying forms of reference data. This reference data can range from still photographs, video, 3D polygonal meshes or even 3D point clouds. The techniques presented here endeavour to provide tools and styles that are not traditionally available to an artist. Copyright © 2012 John Wiley & Sons, Ltd

The Evaluation of Stylized Facial Expressions

Stylized rendering aims to abstract information in an image making it useful not only for artistic but also for visualization purposes. Recent advances in computer graphics techniques have made it possible to render many varieties of stylized imagery efficiently. So far, however, few attempts have been made to characterize the perceptual impact and effectiveness of stylization. In this paper, we report several experiments that evaluate three different stylization techniques in the context of dynamic facial expressions. Going beyond the usual questionnaire approach, the experiments compare the techniques according to several criteria ranging from introspective measures (subjective preference) to task-dependent measures (recognizability, intensity). Our results shed light on how stylization of image contents affects the perception and subjective evaluation of facial expressions

The Creation Process of a Stylized Character in Comparison to a Semi-realistic Character

Abstract I. Introduction a. Thesis statement: What is the process for modeling a stylized character and how does this differ from a semi-realistic character? b. Expanded thesis statement: The two styles differ from start to finish in a variety of ways. I believe that semi-realistic characters require more source material when drawing and modeling; however stylized characters require a different level of creativity and artistic ability in creation. Modeling semi-realistic characters will be more dependent on source images while stylized characters may require special attention with non-standard texture, style, etc. Rendering techniques will also differ when it goes to presenting the final polished versions with the stylized character focusing on rendering styles that flatten the character while the semi realistic character will require rendering techniques that make it seem more real visually