A Shape‐Aware Model for Discrete Texture Synthesis

Landes, Pierre-Edouard; Galerne, Bruno; Hurtut, Thomas

doi:10.1111/cgf.12152

Cited by 28 publications

(31 citation statements)

References 32 publications

(44 reference statements)

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“…The algorithms presented in this paper are therefore closely related to similar works like [6,20,27], but our approach is driven by the data of our user study, and opens up new fields of application.…”

Section: Related Workmentioning

confidence: 99%

How Do Users Map Points Between Dissimilar Shapes?

Hecher

Guerrero²,

Wonka

et al. 2018

IEEE Trans. Visual. Comput. Graphics

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User MappingsPredicted Point Density Fig. 1. We investigate how people map points between dissimilar shapes (left) and show that our findings can be used to define statistical models that allow us to transfer individual objects, like trees (middle), and/or procedural content, like ponds, forests and flowers (right), between dissimilar shapes.Abstract-Finding similar points in globally or locally similar shapes has been studied extensively through the use of various point descriptors or shape-matching methods. However, little work exists on finding similar points in dissimilar shapes. In this paper, we present the results of a study where users were given two dissimilar two-dimensional shapes and asked to map a given point in the first shape to the point in the second shape they consider most similar. We find that user mappings in this study correlate strongly with simple geometric relationships between points and shapes. To predict the probability distribution of user mappings between any pair of simple two-dimensional shapes, two distinct statistical models are defined using these relationships. We perform a thorough validation of the accuracy of these predictions and compare our models qualitatively and quantitatively to well-known shape-matching methods. Using our predictive models, we propose an approach to map objects or procedural content between different shapes in different design scenarios.

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Section: Related Workmentioning

confidence: 99%

How Do Users Map Points Between Dissimilar Shapes?

Hecher

Guerrero²,

Wonka

et al. 2018

IEEE Trans. Visual. Comput. Graphics

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show abstract

“…They have been applied to point and 2D element arrangements [Ijiri et al 2008;Hurtut et al 2009;Öztireli and Gross 2012;Landes et al 2013], to structured content (graphs) such as city layouts [Aliaga et al 2008;Lipp et al 2011] and branching structures [Sibbing et al 2010], and to terrains [Zhou et al 2007]. Some of these methods directly reuse the content in the input examples to generate their results, as is typically done for terrains.…”

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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“…Our algorithm uses this operation-instead of sample-centric neighborhoods as such element-based representation can work better for more complicated structures as shown in [Landes et al 2013]. n(op) can capture relationships within and across elements as well as with contextual operations.…”

Section: Representationmentioning

confidence: 99%

“…Analogous to [Ma et al 2011;Landes et al 2013], the analysis and synthesis of workflow textures hinge on the core formulation of neighborhood similarity. Below, we define similarity in the order of samples, operations, and neighborhoods.…”

Section: Similarity Measurementioning

confidence: 99%

Autocomplete painting repetitions

Xing¹,

Chen

Wei³

2014

ACM Trans. Graph.

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The final outcome is shown in (c). Our system works for both individual strokes (e.g. the detailed hatches) and structured elements (e.g. the windows). Due to the spatial and temporal variations, these sketches are very difficult, if not impossible, to be generated via prior sequential clone systems. AbstractPainting is a major form of content creation, offering unlimited control and freedom of expression. However, it can involve tedious manual repetitions, such as stippling large regions or hatching complex contours. Thus, a central goal in digital painting research is to automate tedious repetitions while allowing user control. Existing methods impose a sequential order, in which a small exemplar is prepared and then cloned through additional gestures. Such sequential mode may break the continuous, spontaneous flow of painting. Moreover, it is more suitable for homogeneous areas than nuanced variations common in real paintings.We present an interactive digital painting system that autocompletes tedious repetitions while preserving nuanced variations and maintaining natural flows. Specifically, users paint as usual, while our system records and analyzes their workflows. When potential repetition is detected, our system predicts what the user might want to draw and offers auto-completes that adjust to the existing shape-color context. Our method eliminates the need for sequential creation-cloning and better adapts to the local painting contexts. Furthermore, users can choose to accept, ignore, or modify those predictions and thus maintain full control. Our method can be considered as the painting analogy of auto-completes in common typing and IDE systems. We demonstrate the quality and usability of our system through painting results and a pilot user study.

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A Shape‐Aware Model for Discrete Texture Synthesis

Cited by 28 publications

References 32 publications

How Do Users Map Points Between Dissimilar Shapes?

How Do Users Map Points Between Dissimilar Shapes?

WorldBrush

Autocomplete painting repetitions

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