Geometric Portrait Stylization
| dc.contributor.author | Bukenberger, Dennis R. | en_US |
| dc.contributor.editor | Linsen, Lars | en_US |
| dc.contributor.editor | Thies, Justus | en_US |
| dc.date.accessioned | 2024-09-09T05:26:54Z | |
| dc.date.available | 2024-09-09T05:26:54Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | Our work extends common pixelization techniques, enabling novel geometric pop-art stylization. We employ dedicated feature analysis to autonomously extract facial features, ensuring the best recognizability of persons and facial expressions in portraits. Additionally, our method includes automated content-related detail level extraction for scenic image content. Based on these detail levels, a hierarchical structure sets the basis for non-uniform pixelization. A joint optimization routine computes a reduced color palette alongside the coarse superpixel segmentation.We propose an adapted modification to common superpixel methods to handle non-uniform sized cells, maintaining a comparable level of detail while allowing for a coarser, more pixelated look. Additionally, this intermediate result serves as the basis for our geometric abstraction by eventually clustering polygonal shapes based on the pixelization. Colors and shapes are derived from the source image to capture and reproduce the most essential details for recognizable characters and facial expressions. We document the theoretical details of our method, discuss and elaborate the possible extensions. Provided results of the intermediate pixelization are compared qualitatively to related methods. Compared to other stylization methods, our resulting geometric abstractions are generated automatically, preserving a high level of relevant details from the source image. Unlike simple filtering techniques or learning-based stylization methods, our approach allows for the incorporation of user input to highlight features. Furthermore, our method stays true to the original image and results in scale-independent vector graphics, rendering it a valuable tool for artists and graphic designers. | en_US |
| dc.description.sectionheaders | Geometry | |
| dc.description.seriesinformation | Vision, Modeling, and Visualization | |
| dc.identifier.doi | 10.2312/vmv.20241203 | |
| dc.identifier.isbn | 978-3-03868-247-9 | |
| dc.identifier.pages | 8 pages | |
| dc.identifier.uri | https://doi.org/10.2312/vmv.20241203 | |
| dc.identifier.uri | https://diglib.eg.org/handle/10.2312/vmv20241203 | |
| dc.publisher | The Eurographics Association | en_US |
| dc.rights | Attribution 4.0 International License | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | CCS Concepts: Computing methodologies → Image processing; Non-photorealistic rendering; Computational photography | |
| dc.subject | Computing methodologies → Image processing | |
| dc.subject | Non | |
| dc.subject | photorealistic rendering | |
| dc.subject | Computational photography | |
| dc.title | Geometric Portrait Stylization | en_US |