Special section on Non-Photorealistic Animation and Rendering (NPAR) 2010

International audienceEditoria

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

Dynamic Stylized Shading Primitives

Honorable Mention in RenderingInternational audienceShading appearance in illustrations, comics and graphic novels is designed to convey illumination, material and surface shape characteristics at once. Moreover, shading may vary depending on different configurations of surface distance, lighting, character expressions, timing of the action, to articulate storytelling or draw attention to a part of an object. In this paper, we present a method that imitates such expressive stylized shading techniques in dynamic 3D scenes, and which offers a simple and flexible means for artists to design and tweak the shading appearance and its dynamic behavior. The key contribution of our approach is to seamlessly vary appearance by using a combination of shading primitives that take into account lighting direction, material characteristics and surface features. We demonstrate their flexibility in a number of scenarios: minimal shading, comics or cartoon rendering, glossy and anisotropic material effects; including a variety of dynamic variations based on orientation, timing or depth. Our prototype implementation combines shading primitives with a layered approach and runs in real-time on the GPU

Towards photo watercolorization with artistic verisimilitude

published_or_final_versio

Line drawings for face portraits from photos using global and local structure based GANs

Despite significant effort and notable success of neural style transfer, it remains challenging for highly abstract styles, in particular line drawings. In this paper, we propose APDrawingGAN++, a generative adversarial network (GAN) for transforming face photos to artistic portrait drawings (APDrawings), which addresses substantial challenges including highly abstract style, different drawing techniques for different facial features, and high perceptual sensitivity to artifacts. To address these, we propose a composite GAN architecture that consists of local networks (to learn effective representations for specific facial features) and a global network (to capture the overall content). We provide a theoretical explanation for the necessity of this composite GAN structure by proving that any GAN with a single generator cannot generate artistic styles like APDrawings. We further introduce a classification-and-synthesis approach for lips and hair where different drawing styles are used by artists, which applies suitable styles for a given input. To capture the highly abstract art form inherent in APDrawings, we address two challenging operations — (1) coping with lines with small misalignments while penalizing large discrepancy and (2) generating more continuous lines — by introducing two novel loss terms: one is a novel distance transform loss with nonlinear mapping and the other is a novel line continuity loss, both of which improve the line quality. We also develop dedicated data augmentation and pre-training to further improve results. Extensive experiments, including a user study, show that our method outperforms state-of-the-art methods, both qualitatively and quantitatively

Dynamic Canvas for Immersive Non-Photorealistic Walkthroughs

International audienceThe static background paper or canvas texture usually used for non-photorealistic animation greatly impedes the sensation of motion and results in a disturbing ``shower door'' effect. We present a method to animate the background canvas for non-photorealistic rendering animations and walkthroughs, which greatly improves the sensation of motion and 3D ``immersion''. The complex motion field induced by the 3D displacement is matched using purely 2D transformations. The motion field of forward translations is approximated using a 2D zoom in the texture, and camera rotation is approximated using 2D translation and rotation. A rolling-ball metaphor is introduced to match the instantaneous 3D motion with a 2D transformation. An infinite zoom in the texture is made possible by using a paper model based on multifrequency solid turbulence. Our results indicate a dramatic improvement over a static background