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Item Enhancing Neural Style Transfer using Patch-Based Synthesis(The Eurographics Association, 2019) Texler, Ondřej; Fišer, Jakub; Lukáč, Mike; Lu, Jingwan; Shechtman, Eli; Sýkora, Daniel; Kaplan, Craig S. and Forbes, Angus and DiVerdi, StephenWe present a new approach to example-based style transfer which combines neural methods with patch-based synthesis to achieve compelling stylization quality even for high-resolution imagery. We take advantage of neural techniques to provide adequate stylization at the global level and use their output as a prior for subsequent patch-based synthesis at the detail level. Thanks to this combination, our method keeps the high frequencies of the original artistic media better, thereby dramatically increases the fidelity of the resulting stylized imagery. We also show how to stylize extremely large images (e.g., 340 Mpix) without the need to run the synthesis at the pixel level, yet retaining the original high-frequency details.Item Defining Hatching in Art(The Eurographics Association, 2019) Philbrick, Greg; Kaplan, Craig S.; Kaplan, Craig S. and Forbes, Angus and DiVerdi, StephenWe define hatching-a drawing technique-as rigorously as possible. A pure mathematical formulation or even a binary this-or-that definition is unreachable, but useful insights come from driving as close as we can. First we explain hatching's purposes. Then we define hatching as the use of patches: groups of roughly parallel curves that form flexible, simple patterns. After elaborating on this definition's parts, we briefly treat considerations for research in expressive rendering.Item Irregular Pebble Mosaics with Sub-Pebble Detail(The Eurographics Association, 2019) Javid, Ali Sattari; Doyle, Lars; Mould, David; Kaplan, Craig S. and Forbes, Angus and DiVerdi, StephenPebble mosaics convey images through an irregular tiling of rounded pebbles. Past work used relatively uniform tile sizes. We show how to create detailed representations of input photographs in a pebble mosaic style; we first create pebble shapes through a variant of k-means, then compute sub-pebble detail with textured, two-tone pebbles.We use a custom distance function to ensure that pebble sizes adapt to local detail and orient to local feature directions, for an overall effect of high fidelity to the input photograph despite the constraints of the pebble style.Item Generating Playful Palettes from Images(The Eurographics Association, 2019) DiVerdi, Stephen; Lu, Jingwan; Echevarria, Jose; Shugrina, Maria; Kaplan, Craig S. and Forbes, Angus and DiVerdi, StephenPlayful Palettes are a recent innovation in how artists can mix, explore, and choose colors in a user interface that combines the benefits of a traditional media painter's palette with non-destructive capabilities of digital tools. We present a technique to generate a Playful Palette that best represents the colors found in an input image, allowing the artist to select colors from the image's gamut, while maintaining full editability of the palette. We show that our approach outperforms recent work in terms of how accurately the image gamut is reproduced, and we present an approximation algorithm that is an order of magnitude faster with an acceptable loss in quality.Item Robotic Painting using Semantic Image Abstraction(The Eurographics Association, 2025) Stroh, Michael; Paetzold, Patrick; Berio, Daniel; Leymarie, Frederic Fol; Kehlbeck, Rebecca; Deussen, Oliver; Berio, Daniel; Bruckert, AlexandreWe present a novel image segmentation and abstraction pipeline tailored to robot painting applications. We address the unique challenges of realizing digital abstractions as physical artistic renderings. Our approach generates adaptive, semantics-based abstractions that balance aesthetic appeal, structural coherence, and practical constraints inherent to robotic systems. By integrating panoptic segmentation with color-based over-segmentation, we partition images into meaningful regions corresponding to semantic objects while providing customizable abstraction levels we optimize for robotic realization. We employ saliency maps and color difference metrics to support automatic parameter selection to guide a merging process that detects and preserves critical object boundaries while simplifying less salient areas. Graph-based community detection further refines the abstraction by grouping regions based on local connectivity and semantic coherence. These abstractions enable robotic systems to create paintings on real canvases with a controlled level of detail and abstraction.