Layered modeling and generation of Pollock’s drip style

Yan, Zheng; Nie, Xuecheng; Meng, Zhaopeng; Feng, Wei; Zhang, Kang

doi:10.1007/s00371-014-0985-7

Cited by 15 publications

(7 citation statements)

References 29 publications

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“…Zheng et al [57] attempt to analyze Jackson Pollock's style, who is an influential modern American painter. He draws his paintings by dripping and pouring on canvases instead of traditional painting methods, as shown in Fig.…”

Section: Style Analysismentioning

confidence: 99%

“…It is difficult or even impossible to come up with rules or observe regular patterns. Based on careful analysis, Zheng et al [57] divide the structures of Pollock's drip paintings into four independent layers, including background layer, irregular layer, line layer and paint drop layer from bottom up as shown in Fig. 22.…”

Section: Layered Approachmentioning

confidence: 99%

“…The design Fig. 22 Layered approach for modeling Pollock's drip style [57] rules could be responsible in generating a variety of basic designs and deep learning methods help enrich the design with the extracted design styles. In this way, each design both satisfies the design principles and has the distinct artistic style.…”

Section: Design Generationmentioning

confidence: 99%

“…Miro's "Ciphers and Constellations in Love with a Woman"Fig. 19A computer generated image of Pollock's "Number 8"[57] …”

mentioning

confidence: 99%

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Computational aesthetics and applications

Zhang

2018

Vis. Comput. Ind. Biomed. Art

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Computational aesthetics, which bridges science and art, is emerging as a new interdisciplinary field. This paper concentrates on two main aspects of computational aesthetics: aesthetic measurement and quantification, generative art, and then proposes a design generation framework. On aesthetic measurement and quantification, we review different types of features used in measurement, the currently used evaluation methods, and their applications. On generative art, we focus on both fractal art and abstract paintings modeled on well-known artists' styles. In general, computational aesthetics exploits computational methods for aesthetic expressions. In other words, it enables computer to appraise beauty and ugliness and also automatically generate aesthetic images. Computational aesthetics has been widely applied to many areas, such as photography, fine art, Chinese handwriting , web design, graphic design, and industrial design. We finally propose a design generation methodology, utilizing techniques from both aesthetic measurements and generative art.

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Section: Style Analysismentioning

confidence: 99%

Section: Layered Approachmentioning

confidence: 99%

Section: Design Generationmentioning

confidence: 99%

“…Miro's "Ciphers and Constellations in Love with a Woman"Fig. 19A computer generated image of Pollock's "Number 8"[57] …”

mentioning

confidence: 99%

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Computational aesthetics and applications

Zhang

2018

Vis. Comput. Ind. Biomed. Art

Self Cite

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show abstract

“…While generating esthetically pleasing results, this methods necessitates heavy user interaction. Most recently, methods were proposed to mimick the styles of speci c artists, such as Picasso [Lian et al, 2016] and Pollock [Zheng et al, 2015], using artistic layers approaches, and Miro [Xiong and Zhang, 2016], using random generation.…”

Section: Related Workmentioning

confidence: 99%