Stroke Based Painterly Rendering

International audienceMany traditional art forms are produced by an artist sequentially placing a set of marks, such as brush strokes, on a canvas. Stroke based Rendering (SBR) is inspired by this process, and underpins many early and contemporary Artistic Stylization algorithms. This Chapter outlines the origins of SBR, and describes key algorithms for placement of brush strokes to create painterly renderings from source images. The chapter explores both local greedy, and global optimization based approaches to stroke placement. The issue of creative control in SBR is also briefly discussed

Towards Simulation of Handmade Painterly Animation

International audienceThis poster presents research in progress towards the simulation of handmade painterly animation. As researches on painterly animation mostly focus on temporal coherence, the generation of an animation that could have been done by hand remains an open challenging problem. To produce an hand made painterly animation, the artist paints on a retro-lighted glass canvas. He creates successive frames one over the other. The artist erase, smears and adds paint to produce each frame. We propose to render painterly animations with a visual aspect tending towards hand made results.To this end, we combines two techniques: an automatic strokes generator and a paint simulator. We observe that previous approaches could not adapt as-is to our goals for the two following reasons:Paint appearance in painterly animation is back lighted, most of paint contrast comes from paint thickness which is usually not compute by paint simulation approaches. Automatic strokes generation make the assumption that a stroke covers underlying strokes without having thickness accumulation, in our case paint thickness quickly increase if we do not apply special treatment. We also investigate new kind of strokes that are only possible with a paint simulation approach as smear and erase strokes

Characterizing and Improving Stability in Neural Style Transfer

Recent progress in style transfer on images has focused on improving the quality of stylized images and speed of methods. However, real-time methods are highly unstable resulting in visible flickering when applied to videos. In this work we characterize the instability of these methods by examining the solution set of the style transfer objective. We show that the trace of the Gram matrix representing style is inversely related to the stability of the method. Then, we present a recurrent convolutional network for real-time video style transfer which incorporates a temporal consistency loss and overcomes the instability of prior methods. Our networks can be applied at any resolution, do not re- quire optical flow at test time, and produce high quality, temporally consistent stylized videos in real-time

Expressive Animated Character Sequences Using Knowledge-Based Painterly Rendering

We propose a technique to enhance emotionalexpressiveness in games and animations. Artists have usedcolors and painting techniques to convey emotions in theirpaintings for many years. Moreover, researchers have foundthat colors and line properties affect users\u27 emotions. Wepropose using painterly rendering for character sequencesin games and animations with a knowledge-based approach. This technique is especially useful for parametric facial sequences. We introduce two parametric authoring tools foranimation and painterly rendering and a method to integrate them into a knowledge-based painterly rendering system. Furthermore, we present the results of a preliminarystudy on using this technique for facial expressions in stillimages. The results of the study show the effect of different color palettes on the intensity perceived for an emotionby users. The proposed technique can provide the animatorwith a depiction tool to enhance the emotional content of acharacter sequence in games and animations

Video Manipulation Techniques for the Protection of Privacy in Remote Presence Systems

Systems that give control of a mobile robot to a remote user raise privacy concerns about what the remote user can see and do through the robot. We aim to preserve some of that privacy by manipulating the video data that the remote user sees. Through two user studies, we explore the effectiveness of different video manipulation techniques at providing different types of privacy. We simultaneously examine task performance in the presence of privacy protection. In the first study, participants were asked to watch a video captured by a robot exploring an office environment and to complete a series of observational tasks under differing video manipulation conditions. Our results show that using manipulations of the video stream can lead to fewer privacy violations for different privacy types. Through a second user study, it was demonstrated that these privacy-protecting techniques were effective without diminishing the task performance of the remote user.Comment: 14 pages, 8 figure