In this paper we present LazyBrush, a novel interactive tool for painting hand-made cartoon drawings and animations. Its key advantage is simplicity and flexibility. As opposed to previous custom tailored approaches [SBv05, QWH06] LazyBrush does not rely on style specific features such as homogenous regions or pattern continuity yet still offers comparable or even less manual effort for a broad class of drawing styles. In addition to this, it is not sensitive to imprecise placement of color strokes which makes painting less tedious and brings significant time savings in the context cartoon animation. LazyBrush originally stems from requirements analysis carried out with professional ink-and-paint illustrators who established a list of useful features for an ideal painting tool. We incorporate this list into an optimization framework leading to a variant of Potts energy with several interesting theoretical properties. We show how to minimize it efficiently and demonstrate its usefulness in various practical scenarios including the ink-and-paint production pipeline.

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This paper presents a novel interactive approach for adding depth information into hand-drawn cartoon images and animations. In comparison to previous depth assignment techniques our solution requires minimal user effort and enables creation of consistent pop-ups in a matter of seconds. Inspired by perceptual studies we formulate a custom tailored optimization framework that tries to mimic the way that a human reconstructs depth information from a single image. Its key advantage is that it completely avoids inputs requiring knowledge of absolute depth and instead uses a set of sparse depth (in)equalities that are much easier to specify. Since these constraints lead to a solution based on quadratic programming that is time consuming to evaluate we propose a simple approximative algorithm yielding similar results with much lower computational overhead. We demonstrate its usefulness in the context of a cartoon animation production pipeline including applications such as enhancement, registration, composition, 3D modelling and stereoscopic display.

Interactive simulation is made possible in many applications by simplifying or culling the finer details that would make real-time performance impossible. This paper examines detail simplification in the specific problem of collision handling for rigid body animation. We present an automated method for calculating consistent collision response at different levels of detail. The mechanism works closely with a system which uses a pre-computed hierarchical volume model for collision detection.

Level of Detail (LOD) techniques for real-time rendering and related perceptual issues have received a lot of attention in recent years. Researchers have also begun to look at the issue of perceptually adaptive techniques for plausible physical simulations. In this article, we are particularly interested in the problem of realistic collision simulation in scenes where large numbers of objects are colliding and processing must occur in real-time. An interruptible and therefore degradable collision-handling mechanism is used and the perceptual impact of this degradation is explored. We look for ways in which we can optimize the realism of such simulations and describe a series of psychophysical experiments that investigate different factors affecting collision perception, including eccentricity, separation, distractors, causality, and accuracy of physical response. Finally, strategies for incorporating these factors into a perceptually adaptive real-time simulation of large numbers of visually similar objects are presented.