Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/105958
DC Field | Value | Language |
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dc.contributor | Department of Computing | - |
dc.contributor | School of Design | - |
dc.creator | Wang, Q | - |
dc.creator | Guo, C | - |
dc.creator | Dai, HN | - |
dc.creator | Li, P | - |
dc.date.accessioned | 2024-04-23T04:32:36Z | - |
dc.date.available | 2024-04-23T04:32:36Z | - |
dc.identifier.issn | 2096-0433 | - |
dc.identifier.uri | http://hdl.handle.net/10397/105958 | - |
dc.language.iso | en | en_US |
dc.publisher | SpringerOpen | en_US |
dc.rights | © The Author(s) 2023. | en_US |
dc.rights | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creativecommons.org/licenses/by/4.0/. Other papers from this open access journal are available free of charge from http://www.springer.com/journal/41095. To submit a manuscript, please go to https://www. editorialmanager.com/cvmj. | en_US |
dc.rights | The following publication Wang, Q., Guo, C., Dai, HN. et al. Stroke-GAN Painter: Learning to paint artworks using stroke-style generative adversarial networks. Comp. Visual Media 9, 787–806 (2023) is available at https://doi.org/10.1007/s41095-022-0287-3. | en_US |
dc.subject | AI painting | en_US |
dc.subject | Artistic style | en_US |
dc.subject | Painting strokes | en_US |
dc.title | Stroke-GAN Painter : learning to paint artworks using stroke-style generative adversarial networks | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 787 | - |
dc.identifier.epage | 806 | - |
dc.identifier.volume | 9 | - |
dc.identifier.issue | 4 | - |
dc.identifier.doi | 10.1007/s41095-022-0287-3 | - |
dcterms.abstract | It is a challenging task to teach machines to paint like human artists in a stroke-by-stroke fashion. Despite advances in stroke-based image rendering and deep learning-based image rendering, existing painting methods have limitations: they (i) lack flexibility to choose different art-style strokes, (ii) lose content details of images, and (iii) generate few artistic styles for paintings. In this paper, we propose a stroke-style generative adversarial network, called Stroke-GAN, to solve the first two limitations. Stroke-GAN learns styles of strokes from different stroke-style datasets, so can produce diverse stroke styles. We design three players in Stroke-GAN to generate pure-color strokes close to human artists’ strokes, thereby improving the quality of painted details. To overcome the third limitation, we have devised a neural network named Stroke-GAN Painter, based on Stroke-GAN; it can generate different artistic styles of paintings. Experiments demonstrate that our artful painter can generate various styles of paintings while well-preserving content details (such as details of human faces and building textures) and retaining high fidelity to the input images. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Computational visual media, Dec. 2023, v. 9, no. 4, p. 787-806 | - |
dcterms.isPartOf | Computational visual media | - |
dcterms.issued | 2023-12 | - |
dc.identifier.scopus | 2-s2.0-85149786108 | - |
dc.identifier.eissn | 2096-0662 | - |
dc.description.validate | 202404 bcch | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | Hong Kong Institute of Business Studies (HKIBS) Research Seed Fund; Hong Kong Polytechnic University | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | CC | en_US |
Appears in Collections: | Journal/Magazine Article |
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File | Description | Size | Format | |
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s41095-022-0287-3.pdf.pdf | 12.67 MB | Adobe PDF | View/Open |
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