Real-time cartoon-like stylization of AR video streams on the GPU

The ultimate goal of many applications of augmented reality is to immerse the user into the augmented scene, which is enriched with virtual models. In order to achieve this immersion, it is necessary to create the visual impression that the graphical objects are a natural part of the user’s environment. Producing this effect with conventional computer graphics algorithms is a complex task. Various rendering artifacts in the three-dimensional graphics create a noticeable visual discrepancy between the real background image and virtual objects. We have recently proposed a novel approach to generating an augmented video stream. With this new method, the output images are a non-photorealistic reproduction of the augmented environment. Special stylization methods are applied to both the background camera image and the virtual objects. This way the visual realism of both the graphical foreground and the real background image is reduced, so that they are less distinguishable from each other. Here, we present a new method for the cartoon-like stylization of augmented reality images, which uses a novel post-processing filter for cartoon-like color segmentation and high-contrast silhouettes. In order to make a fast postprocessing of rendered images possible, the programmability of modern graphics hardware is exploited. We describe an implementation of the algorithm using the OpenGL Shading Language. The system is capable of generating a stylized augmented video stream of high visual quality at real-time frame rates. As an example application, we demonstrate the visualization of dinosaur bone datasets in stylized augmented reality

A pointillism style for the non-photorealistic display of augmented reality scenes

The ultimate goal of augmented reality is to provide the user with a view of the surroundings enriched by virtual objects. Practically all augmented reality systems rely on standard real-time rendering methods for generating the images of virtual scene elements. Although such conventional computer graphics algorithms are fast, they often fail to produce sufficiently realistic renderings. The use of simple lighting and shading methods, as well as the lack of knowledge about actual lighting conditions in the real surroundings, cause virtual objects to appear artificial. We have recently proposed a novel approach for generating augmented reality images. Our method is based on the idea of applying stylization techniques for reducing the visual realism of both the camera image and the virtual graphical objects. Special non-photorealistic image filters are applied to the camera video stream. The virtual scene elements are rendered using non-photorealistic rendering methods. Since both the camera image and the virtual objects are stylized in a corresponding way, they appear very similar. As a result, graphical objects can become indistinguishable from the real surroundings. Here, we present a new method for the stylization of augmented reality images. This approach generates a painterly "brush stroke" rendering. The resulting stylized augmented reality video frames look similar to paintings created in the "pointillism" style. We describe the implementation of the camera image filter and the non-photorealistic renderer for virtual objects. These components have been newly designed or adapted for this purpose. They are fast enough for generating augmented reality images in real-time and are customizable. The results obtained using our approach are very promising and show that it improves immersion in augmented reality

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

Measuring the Discernability of Virtual Objects in Conventional and Stylized Augmented Reality

In augmented reality, virtual graphical objects are overlaid over the real environment of the observer. Conventional augmented reality systems normally use standard real-time rendering methods for generating the graphical representations of virtual objects. These renderings contain the typical artifacts of computer generated graphics, e.g., aliasing caused by the rasterization process and unrealistic, manually configured illumination models. Due to these artifacts, virtual objects look artifical and can easily be distinguished from the real environment. A different approach to generating augmented reality images is the basis of stylized augmented reality [FBS05c]. Here, similar types of artistic or illustrative stylization are applied to the virtual objects and the camera image of the real enviroment. Therefore, real and virtual image elements look significantly more similar and are less distinguishable from each other. In this paper, we present the results of a psychophysical study on the effectiveness of stylized augmented reality. In this study, a number of participants were asked to decide whether objects shown in images of augmented reality scenes are virtual or real. Conventionally rendered as well as stylized augmented reality images and short video clips were presented to the participants. The correctness of the participants' responses and their reaction times were recorded. The results of our study show that an equalized level of realism is achieved by using stylized augmented reality, i.e., that it is significantly more difficult to distinguish virtual objects from real objects

From Analog to Virtual: Visual Stylizations of Humanoid Characters Across Media

Visual stylization and its impact on different aspects of the perception of digital human beings are commonly debated. This study investigated how fictional and non-fictional characters are represented in various media from the perspective of digital humanoid character design. Based on Zangwill's theory of Moderate Aesthetic Formalism, this study focused on the formalistic aspect of visual analysis and interpretation of media artifacts ranging from older media such as paintings to newer media, such as animations, interactive video games and mobile apps. This paper also explores several case studies of how humanoid digital characters are represented via visual stylizations across different media. This article underlines the importance of visual stylization as an opportunity to find unique and innovative ways of communicating with visual means

Learning to Incorporate Texture Saliency Adaptive Attention to Image Cartoonization

Image cartoonization is recently dominated by generative adversarial networks (GANs) from the perspective of unsupervised image-to-image translation, in which an inherent challenge is to precisely capture and sufficiently transfer characteristic cartoon styles (e.g., clear edges, smooth color shading, abstract fine structures, etc.). Existing advanced models try to enhance cartoonization effect by learning to promote edges adversarially, introducing style transfer loss, or learning to align style from multiple representation space. This paper demonstrates that more distinct and vivid cartoonization effect could be easily achieved with only basic adversarial loss. Observing that cartoon style is more evident in cartoon-texture-salient local image regions, we build a region-level adversarial learning branch in parallel with the normal image-level one, which constrains adversarial learning on cartoon-texture-salient local patches for better perceiving and transferring cartoon texture features. To this end, a novel cartoon-texture-saliency-sampler (CTSS) module is proposed to dynamically sample cartoon-texture-salient patches from training data. With extensive experiments, we demonstrate that texture saliency adaptive attention in adversarial learning, as a missing ingredient of related methods in image cartoonization, is of significant importance in facilitating and enhancing image cartoon stylization, especially for high-resolution input pictures.Comment: Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7183-7207, 202