Self Tuning Texture Optimization
| dc.contributor.author | Kaspar, Alexandre | en_US |
| dc.contributor.author | Neubert, Boris | en_US |
| dc.contributor.author | Lischinski, Dani | en_US |
| dc.contributor.author | Pauly, Mark | en_US |
| dc.contributor.author | Kopf, Johannes | en_US |
| dc.contributor.editor | Olga Sorkine-Hornung and Michael Wimmer | en_US |
| dc.date.accessioned | 2015-04-16T07:44:52Z | |
| dc.date.available | 2015-04-16T07:44:52Z | |
| dc.date.issued | 2015 | en_US |
| dc.description.abstract | The goal of example-based texture synthesis methods is to generate arbitrarily large textures from limited exemplars in order to fit the exact dimensions and resolution required for a specific modeling task. The challenge is to faithfully capture all of the visual characteristics of the exemplar texture, without introducing obvious repetitions or unnatural looking visual elements. While existing non-parametric synthesis methods have made remarkable progress towards this goal, most such methods have been demonstrated only on relatively low-resolution exemplars. Real-world high resolution textures often contain texture details at multiple scales, which these methods have difficulty reproducing faithfully. In this work, we present a new general-purpose and fully automatic selftuning non-parametric texture synthesis method that extends Texture Optimization by introducing several key improvements that result in superior synthesis ability. Our method is able to self-tune its various parameters and weights and focuses on addressing three challenging aspects of texture synthesis: (i) irregular large scale structures are faithfully reproduced through the use of automatically generated and weighted guidance channels; (ii) repetition and smoothing of texture patches is avoided by new spatial uniformity constraints; (iii) a smart initialization strategy is used in order to improve the synthesis of regular and near-regular textures, without affecting textures that do not exhibit regularities. We demonstrate the versatility and robustness of our completely automatic approach on a variety of challenging high-resolution texture exemplars. | en_US |
| dc.description.number | 2 | en_US |
| dc.description.sectionheaders | Colors and Textures | en_US |
| dc.description.seriesinformation | Computer Graphics Forum | en_US |
| dc.description.volume | 34 | en_US |
| dc.identifier.doi | 10.1111/cgf.12565 | en_US |
| dc.identifier.pages | 349-359 | en_US |
| dc.identifier.uri | https://doi.org/10.1111/cgf.12565 | en_US |
| dc.publisher | The Eurographics Association and John Wiley & Sons Ltd. | en_US |
| dc.subject | I.3.3 [Computer Graphics] | en_US |
| dc.subject | Picture/Image Generation | en_US |
| dc.subject | Line and curve generation | en_US |
| dc.title | Self Tuning Texture Optimization | en_US |