CUSTOMIZING PAINTERLY RENDERING STYLES USING STROKE PROCESSES - PowerPoint PPT Presentation

CUSTOMIZING PAINTERLY RENDERING STYLES USING STROKE PROCESSES Mingtian Zhao, Song-Chun Zhu University of California, Los Angeles Lotus Hill Institute http://bit.ly/stroke-processes

Stroke-Based Painterly Rendering • Brush Modeling • Mostly Objective • Existing Methods: Procedural, Example-Based • [Strassmann 86; Cockshott et al. 92; Hertzmann 98, 02; Baxter et al. 01, 04] • Stroke Placement • Highly Subjective: Styles, Feelings, etc. • Existing Methods: Greedy, Global Energy Optimization • [Hertzmann 98, 01; Collomosse et al. 02; Hays and Essa 04; Zeng et al. 09] • We study the latter in this paper.

The Problem: Language Gap • Artists’ Language • Vibrant Colors • Gestural Strokes • Sense of Illumination/Motion • Computer Scientists’ Language • Color Vector (RGB, between 0 and 255) • Stroke Length/Width (in pixels) • D ifficult to map artists’ descriptions to rendering parameters

Characteristics • Observation • Local contrast is important (the “tempo”) • The Bridge: Spatial Statistics • Forestry and Plant Ecology • Epidemiology • Seismology • … • Our Approach: Stroke Processes • Marked Point Process for Stroke Layout • Reaction-Diffusion for Stroke Attributes

Stroke Processes • Stroke Element • Position • Orientation • Size • Color • Stroke Neighborhood Graph • Second-Order Features • Computing Tools • Perceptual Characteristics/Dimensions • Quantitative Evaluations • Rendering Parameters

Perceptual Dimensions • Density Low Density High Density

Perceptual Dimensions • Density • Non-Uniformity Low Non-Uniformity High Non-Uniformity

Perceptual Dimensions • Density • Non-Uniformity • Local Isotropy High Local Isotropy Low Local Isotropy Histograms of Orientation Differences

Perceptual Dimensions • Density • Non-Uniformity • Local Isotropy • Coarseness Low Coarseness High Coarseness

Perceptual Dimensions • Density • Non-Uniformity • Local Isotropy • Coarseness • Size Contrast Low Size Contrast High Size Contrast Histograms of Size Differences

Perceptual Dimensions • Density • Non-Uniformity • Local Isotropy • Coarseness • Size Contrast • Lightness Contrast • Chroma Contrast Low Hue Contrast High Hue Contrast • Hue Contrast Histograms of Hue Differences

Example: Lightness Contrast (Fig.1)

Software Interface

Layout Process • Non-stationary Hard-core Poisson • Segmentation [Zeng et al. 09] • Salience Map • Steerable Filtering • Stroke Density Map • Histogram Matching • Spatial Sampling • Rejection Sampling

Stroke Neighborhood Graph • Initialization • Orientation Field [Zeng et al. 09] • Local 2D Cartesian Coordinates • Computing Nearest Neighbors • One in each of the four quadrants • Anisotropic

Attribute Processes • Ensemble Statistics ---- Gibbs Energy ---- Diffusion Process • Orientation • Size • Color • Hue: Periodic, similar to orientation • Lightness and Chroma: Aperiodic, similar to size

Experiments (Fig.8) • (a) neutral • (b) high size contrast (leaves) and • low local isotropy (background) • (c) high hue contrast (a) (b) (c)

Experiments (Fig.9) • (a) neutral • (b) low density and high coarseness (wall) • (c) high size contrast and high hue contrast • (wall, shelf, can in the middle) (a) (b) (c)

Experiments (Fig.10) • (a) neutral • (b) high lightness contrast • (c) low local isotropy (a) (b) (c)

Summary • Customize painterly rendering styles via eight intuitive parameters • Emphasize local contrasts • Interactive software with real-time feedback • Can simulate styles difficult to achieve using previous methods • Future Work • Parameter Space Analysis • Neighborhood Design

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