Learning hatching for pen-and-ink illustration of surfaces

Kalogerakis, Evangelos; Nowrouzezahrai, Derek; Breslav, Simon; Hertzmann, Aaron

doi:10.1145/2077341.2077342

Cited by 80 publications

(58 citation statements)

References 39 publications

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“…It is thus very difficult for these methods to convincingly reproduce the stylistic properties and variations present in hand-drawn hatchings. For this reason, Kalogerakis et al [2] recently proposed to learn hatching styles from example drawings. While Kalogerakis et al's [2] work serves as an inspiration of our own, we improve on it by using analytical representations of hatching patches and hatching strokes, by modeling stroke distance interrelationships in more detail, as well as by specifically permitting interaction.…”

Section: Related Workmentioning

confidence: 99%

“…For this reason, Kalogerakis et al [2] recently proposed to learn hatching styles from example drawings. While Kalogerakis et al's [2] work serves as an inspiration of our own, we improve on it by using analytical representations of hatching patches and hatching strokes, by modeling stroke distance interrelationships in more detail, as well as by specifically permitting interaction. We explain how we deviate from Kalogerakis et al's [2] work in more detail below.…”

Section: Related Workmentioning

confidence: 99%

“…We aim at incorporating human virtuosity and illustration skills into the computer generation of pen-and-ink hatching illustrations to improve upon the aesthetic appeal and illustration effectiveness of such imagery. Recently, Kalogerakis et al [2] presented an approach that shares our goals. In this paper we present an approach that uses an explicit representation of drawing elements, in contrast to the pixel-based approach chosen by Kalogerakis et al [2].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, Kalogerakis et al [2] presented an approach that shares our goals. In this paper we present an approach that uses an explicit representation of drawing elements, in contrast to the pixel-based approach chosen by Kalogerakis et al [2]. This representation allows us to transfer drawing characteristics on higher levels than a pixel grid and to provide user interactions for adjusting the resulting illustrations.…”

Section: Introductionmentioning

confidence: 99%

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Interactive example-based hatching

Gerl

Isenberg

2013

Computers & Graphics

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a b s t r a c tWe present an approach for interactively generating pen-and-ink hatching renderings based on handdrawn examples. We aim to overcome the regular and synthetic appearance of the results of existing methods by incorporating human virtuosity and illustration skills in the computer generation of such imagery. To achieve this goal, we propose to integrate an automatic style transfer with user interactions. This approach leverages the potential of example-based hatching while giving users the control and creative freedom to enhance the aesthetic appearance of the results. Using a scanned-in hatching illustration as input, we use image processing and machine learning methods to learn a model of the drawing style in the example illustration. We then apply this model to semi-automatically synthesize hatching illustrations of 3D meshes in the learned drawing style. In the learning stage, we first establish an analytical description of the hand-drawn example illustration using image processing. A 3D scene registered with the example drawing allows us to infer object-space information related to the 2D drawing elements. We employ a hierarchical style transfer model that captures drawing characteristics on four levels of abstraction, which are global, patch, stroke, and pixel levels. In the synthesis stage, an explicit representation of hatching strokes and hatching patches enables us to synthesize the learned hierarchical drawing characteristics. Our representation makes it possible to directly and intuitively interact with the hatching illustration. Amongst other interactions, users of our system can brush with patches of hatching strokes onto a 3D mesh. This interaction capability allows illustrators who are working with our system to make use of their artistic skills. Furthermore, the proposed interactions allow people without a background in hatching to interactively generate visually appealing hatching illustrations.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Interactive example-based hatching

Gerl

Isenberg

2013

Computers & Graphics

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“…Sophisticated user interfaces also help make customization easier [O'Donovan and Hertzmann 2012]. Other approaches use example-based control by copying pixels from an exemplar image [Hertzmann et al 2001] or by learning hatching styles from a drawn example [Kalogerakis et al 2011]. In all cases, the primary focus is controlling stroke synthesis, rather than direct specification of the final result.…”

Section: Related Workmentioning

confidence: 99%

Authoring and animating painterly characters

et al. 2013

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Artists explore the visual style of animated characters through 2D concept art, since it affords them a nearly unlimited degree of creative freedom. Realizing the desired visual style, however, within the 3D character animation pipeline is often impossible, since artists must work within the technical limitations of the pipeline toolset. In order to expand the range of possible visual styles for digital characters, our research aims to incorporate the expressiveness afforded by 2D concept painting into the computer animation pipeline as a core component of character authoring and animation. While prior 3D painting methods focus on static geometry or simple animations, we develop tools for the more difficult task of character animation. Our system shows how 3D stroke-based paintings can be deformed using standard rigging tools. We also propose a configuration-space keyframing algorithm for authoring stroke effects that depend on scene variables such as character pose or light position. During animation, our system supports stroke-based temporal keyframing for one-off effects. Our primary technical contribution is a novel interpolation scheme for configuration-space keyframing that ensures smooth, controllable results. We demonstrate several characters authored with our system that exhibit painted effects difficult to achieve with traditional animation tools.

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Non-photorealistic Shading and Hatching

Ostromoukhov

2012

Computational Imaging and Vision

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Seeing the external world is an essential ability of the human being. It ensures the most vital survival functions such as avoiding predators, search of food or reproduction. Our visual system is a fine result of millions of years of evolution and constant improvement. It is not surprising that it is extremely fast and provides to our brain the information about spatial structure of the external world, as well as the information about relative movement of external objects and the observer himself. The science of vision (neuroscience, psychology, computer vision) studies the process of perception of the external world, starting from retinal stimuli and ending up in a complete spatiotemporal internal brain image. According to the most influential Marr computational model of vision, the whole process is divided into several stages: from unstructured retinal image to primal 2.5 sketch, then to object-based categorial model, essential to develop an understanding of the scene. This process is extremely complex, with several feedback loops [19,26]. To facilitate the task at early stages of the perception process, our vision system uses, as visual cues, various pieces of information available in the scene: variations of tone due to natural distribution of light in the scene, relative size and foreshortening of objects due to the perspective, presence of edges due to particular viewing point, etc. It has been discovered that some of these perception functions are 'wired' (e.g., lateral inhibition in the retinal processing, which facilitates edge detection); others are high-level functions of our brain, which are learned from experience [19,26].Artists were always interested in depicting the external world. Early examples of such depictions can be found in Lascaux caves; they are estimated to be 17,300 years V. Ostromoukhov ( ) CNRS/Université Claude Bernard Lyon 1, 43, bd du 11 novembre 1918,

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Learning hatching for pen-and-ink illustration of surfaces

Cited by 80 publications

References 39 publications

Interactive example-based hatching

Interactive example-based hatching

Authoring and animating painterly characters

Non-photorealistic Shading and Hatching

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