ArtStroke-GAN: A Unified Framework for Interpreting Watercolor Aesthetics via Personalized Stroke-Based Graphic Language
Painting is an expressive art form traditionally associated with considerable technical skill and prolonged training, making it largely inaccessible to the general public. Computational stroke-based rendering (SBR) approaches have emerged to simulate human-like artistic processes digitally; however,...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11123435/ |
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| author | Laixi Zheng |
| author_facet | Laixi Zheng |
| author_sort | Laixi Zheng |
| collection | DOAJ |
| description | Painting is an expressive art form traditionally associated with considerable technical skill and prolonged training, making it largely inaccessible to the general public. Computational stroke-based rendering (SBR) approaches have emerged to simulate human-like artistic processes digitally; however, current methods encounter notable limitations, such as spatial inconsistencies, stylistic discontinuities, and insufficient control over fine-grained stroke details. To address these challenges, this paper introduces ArtStroke-GAN, a novel generative adversarial network framework designed to synthesize realistic and personalized watercolor paintings. The proposed framework employs a modular three-part GAN architecture consisting of a stroke-generation network, a dedicated color-enhancement module, and an adversarial discriminator. Adaptive spline-based stroke modeling and iterative, semantic-aware stroke optimization strategies are integrated, enabling progressive refinement from coarse to detailed artistic representations. Experimental evaluations conducted on diverse image datasets (CelebA and ImageNet) demonstrate that ArtStroke-GAN achieves superior performance compared to state-of-the-art methods, including Stroke-GAN, Paint Transformer, and Neural-Paint, across quantitative metrics such as LPIPS, FID, style loss, and content fidelity. Qualitative assessments further confirm its ability to produce aesthetically coherent, structurally consistent, and stylistically diverse watercolor renderings, significantly advancing the capability of computational art generation frameworks. |
| format | Article |
| id | doaj-art-b2725b0318124f8fb7088cc780e44b76 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-b2725b0318124f8fb7088cc780e44b762025-08-22T23:16:57ZengIEEEIEEE Access2169-35362025-01-011314495414497410.1109/ACCESS.2025.359818311123435ArtStroke-GAN: A Unified Framework for Interpreting Watercolor Aesthetics via Personalized Stroke-Based Graphic LanguageLaixi Zheng0https://orcid.org/0009-0004-5280-1296School of Painting and Drawing, Guangzhou Academy of Fine Arts, Guangzhou, ChinaPainting is an expressive art form traditionally associated with considerable technical skill and prolonged training, making it largely inaccessible to the general public. Computational stroke-based rendering (SBR) approaches have emerged to simulate human-like artistic processes digitally; however, current methods encounter notable limitations, such as spatial inconsistencies, stylistic discontinuities, and insufficient control over fine-grained stroke details. To address these challenges, this paper introduces ArtStroke-GAN, a novel generative adversarial network framework designed to synthesize realistic and personalized watercolor paintings. The proposed framework employs a modular three-part GAN architecture consisting of a stroke-generation network, a dedicated color-enhancement module, and an adversarial discriminator. Adaptive spline-based stroke modeling and iterative, semantic-aware stroke optimization strategies are integrated, enabling progressive refinement from coarse to detailed artistic representations. Experimental evaluations conducted on diverse image datasets (CelebA and ImageNet) demonstrate that ArtStroke-GAN achieves superior performance compared to state-of-the-art methods, including Stroke-GAN, Paint Transformer, and Neural-Paint, across quantitative metrics such as LPIPS, FID, style loss, and content fidelity. Qualitative assessments further confirm its ability to produce aesthetically coherent, structurally consistent, and stylistically diverse watercolor renderings, significantly advancing the capability of computational art generation frameworks.https://ieeexplore.ieee.org/document/11123435/Deep learninggenerative adversarial networksvisual aestheticsdigital paintingartistic image generation |
| spellingShingle | Laixi Zheng ArtStroke-GAN: A Unified Framework for Interpreting Watercolor Aesthetics via Personalized Stroke-Based Graphic Language IEEE Access Deep learning generative adversarial networks visual aesthetics digital painting artistic image generation |
| title | ArtStroke-GAN: A Unified Framework for Interpreting Watercolor Aesthetics via Personalized Stroke-Based Graphic Language |
| title_full | ArtStroke-GAN: A Unified Framework for Interpreting Watercolor Aesthetics via Personalized Stroke-Based Graphic Language |
| title_fullStr | ArtStroke-GAN: A Unified Framework for Interpreting Watercolor Aesthetics via Personalized Stroke-Based Graphic Language |
| title_full_unstemmed | ArtStroke-GAN: A Unified Framework for Interpreting Watercolor Aesthetics via Personalized Stroke-Based Graphic Language |
| title_short | ArtStroke-GAN: A Unified Framework for Interpreting Watercolor Aesthetics via Personalized Stroke-Based Graphic Language |
| title_sort | artstroke gan a unified framework for interpreting watercolor aesthetics via personalized stroke based graphic language |
| topic | Deep learning generative adversarial networks visual aesthetics digital painting artistic image generation |
| url | https://ieeexplore.ieee.org/document/11123435/ |
| work_keys_str_mv | AT laixizheng artstrokeganaunifiedframeworkforinterpretingwatercoloraestheticsviapersonalizedstrokebasedgraphiclanguage |