Autocomplete painting repetitions

Xing, Junliang; Chen, Hsiang-Ting; Wei, Li-Yi

doi:10.1145/2661229.2661247

Cited by 52 publications

(55 citation statements)

References 44 publications

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“…Most general geometric texture synthesis methods are originally designed for off‐line texturing of defined regions. Recent efforts utilize and extend texture synthesis methods for generating repeated patterns in paintings [KIZD12a, LBDF13, LFB*13, KCG*14, LBW*14, XCW14]. However, these techniques are designed for particular interaction scenarios and output.…”

Section: Related Workmentioning

confidence: 99%

“…This translates into critical dependence on initial distribution of point samples, and non‐trivial neighborhood definition and matching methods, which can result in unsatisfactory synthesis especially when continuous structures are desired. 3) Content creation with repetitive structures remains to be a challenge since the current methods are designed for off‐line texture synthesis and do not support interactive artistic control for general domains [MWT11, LGH13, XCW14].…”

Section: Introductionmentioning

confidence: 99%

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Example Based Repetitive Structure Synthesis

Roveri

Öztireli

Martín

et al. 2015

Computer Graphics Forum

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Figure 1: Our example-based structure synthesis method can be used to generate structures with discrete elements (left), continuous geometries (middle), and their mixtures (right); on different domains such as surfaces, bounding volumes, or curves. AbstractWe present an example based geometry synthesis approach for generating general repetitive structures. Our model is based on a meshless representation, unifying and extending previous synthesis methods. Structures in the example and output are converted into a functional representation, where the functions are defined by point locations and attributes. We then formulate synthesis as a minimization problem where patches from the output function are matched to those of the example. As compared to existing repetitive structure synthesis methods, the new algorithm offers several advantages. It handles general discrete and continuous structures, and their mixtures in the same framework. The smooth formulation leads to employing robust optimization procedures in the algorithm. Equipped with an accurate patch similarity measure and dedicated sampling control, the algorithm preserves local structures accurately, regardless of the initial distribution of output points. It can also progressively synthesize output structures in given subspaces, allowing users to interactively control and guide the synthesis in real-time. We present various results for continuous/discrete structures and their mixtures, residing on curves, submanifolds, volumes, and general subspaces, some of which are generated interactively.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Example Based Repetitive Structure Synthesis

Roveri

Öztireli

Martín

et al. 2015

Computer Graphics Forum

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“…Research on digital brushes includes methods for painting directly from casually-captured texture exemplars [72][73][74], RealBrush [75] which synthesizes from carefully captured natural media exemplars, and "autocomplete" of repetitive strokes in paintings [76]. Ritter et al [73] initially developed a framework that allows artists to paint with textures on a target image by sampling textures directly from an exemplar image.…”

Section: Synthesis For Specialized Domainsmentioning

confidence: 99%

“…13. The autocompletion of repetitive painting strokes [76] works by detecting repetitive painting operations, and suggesting an autocompletion to users if repetition is detected. Strokes are represented in a curve representation which is sampled so that neighborhood matching can be performed between collections of samples.…”

Section: Synthesis For Specialized Domainsmentioning

confidence: 99%

A survey of the state-of-the-art in patch-based synthesis

Barnes

Zhang

2017

Comp. Visual Media

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This paper surveys the state-of-the-art of research in patch-based synthesis. Patch-based methods synthesize output images by copying small regions from exemplar imagery. This line of research originated from an area called "texture synthesis", which focused on creating regular or semi-regular textures from small exemplars. However, more recently, much research has focused on synthesis of larger and more diverse imagery, such as photos, photo collections, videos, and light fields. Additionally, recent research has focused on customizing the synthesis process for particular problem domains, such as synthesizing artistic or decorative brushes, synthesis of rich materials, and synthesis for 3D fabrication. This report investigates recent papers that follow these themes, with a particular emphasis on papers published since 2009, when the last survey in this area was published. This survey can serve as a tutorial for readers who are not yet familiar with these topics, as well as provide comparisons between these papers, and highlight some open problems in this area.

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“…These methods are restricted to their specific category of content; they do not enable the inverse modeling of complex scenes where several types of content affect each other. Close to our work is a recent paper of Xing et al [2014], who present an approach for autocompleting stylized repetitions learned from an input model. However, their approach is limited to 2D polygons.…”

Section: Related Workmentioning

confidence: 99%

WorldBrush

et al. 2015

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Figure 1: An example model (left) is used to learn parameters of distributions of trees, grass, and rocks constrained by the terrain's slope. Our model then uses these parameters to create consistent content in a larger region, using copy-paste or with user-controlled interactive brushes (center). Various adapted editing operations are introduced, inspired by painting tools, such as interpolation of parameters, that allow for creation of varying islands with trees (right). AbstractWe present a novel approach for the interactive synthesis and editing of virtual worlds. Our method is inspired by painting operations and uses methods for statistical example-based synthesis to automate content synthesis and deformation. Our real-time approach takes a form of local inverse procedural modeling based on intermediate statistical models: selected regions of procedurally and manually constructed example scenes are analyzed, and their parameters are stored as distributions in a palette, similar to colors on a painter's palette. These distributions can then be interactively applied with brushes and combined in various ways, like in painting systems. Selected regions can also be moved or stretched while maintaining the consistency of their content. Our method captures both distributions of elements and structured objects, and models their interactions. Results range from the interactive editing of 2D artwork maps to the design of 3D virtual worlds, where constraints set by the terrain's slope are also taken into account.

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Autocomplete painting repetitions

Cited by 52 publications

References 44 publications

Example Based Repetitive Structure Synthesis

Example Based Repetitive Structure Synthesis

A survey of the state-of-the-art in patch-based synthesis

WorldBrush

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