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学者姓名:王玮琦
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Abstract :
寿山石薄意雕刻作品素以“重典雅、工精微、近画理”而著称,融诗、书、画于一体,文人画的融入更令其焕发出新的生命力。在寿山石料的选择、题材内容的斟酌和思想情感的呈现等方面,寿山石雕作品都充分体现出了文人式的艺术表达。本文恰以文人画为切入点,从两种艺术形式的融合与表现出发,对文人画在寿山石薄意雕刻艺术中的审美表现进行深入的研究与探讨,以深入了解文人画与寿山石薄意雕刻两者的内在意蕴。
Keyword :
审美表现 审美表现 寿山石雕 寿山石雕 文人画 文人画 薄意 薄意
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GB/T 7714 | 王玮琦 , 魏亚楠 . 文人画在寿山石薄意雕刻艺术中的审美表现 [J]. | 雕塑 , 2024 , PageCount-页数: 4 (04) : 74-77 . |
MLA | 王玮琦 等. "文人画在寿山石薄意雕刻艺术中的审美表现" . | 雕塑 PageCount-页数: 4 . 04 (2024) : 74-77 . |
APA | 王玮琦 , 魏亚楠 . 文人画在寿山石薄意雕刻艺术中的审美表现 . | 雕塑 , 2024 , PageCount-页数: 4 (04) , 74-77 . |
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本文着重论述将中国革命历程中积淀的红色文化资源融入高校浮雕艺术创作教学体系的实现路径。通过对红色文化在浮雕艺术实践中的融入价值与方法进行深入剖析,尝试构建一系列教学策略框架。此教学模式的设计,意在使学生在掌握雕塑技艺精髓的同时,深刻体悟红色文化的精神内核,进而强化其爱国主义精神与社会责任担当,力求达成艺术教育与思想政治教育两者的有机交融与相互促进,实现个人艺术修养与家国情怀的双重提升。
Keyword :
教学研究 教学研究 文化自信 文化自信 浮雕创作 浮雕创作 红色文化 红色文化
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GB/T 7714 | 王玮琦 . 红色文化元素融入浮雕创作的教学研究 [J]. | 雕塑 , 2024 , PageCount-页数: 2 (06) : 78-79 . |
MLA | 王玮琦 . "红色文化元素融入浮雕创作的教学研究" . | 雕塑 PageCount-页数: 2 . 06 (2024) : 78-79 . |
APA | 王玮琦 . 红色文化元素融入浮雕创作的教学研究 . | 雕塑 , 2024 , PageCount-页数: 2 (06) , 78-79 . |
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With the continuous development of machine learning in the field of graphics and images, there have been many studies on the implementation methods of real painting simulation. Because there are many places of cross integration between different disciplines, the application scope of machine learning is becoming wider and wider. Therefore, this study uses the k-mean clustering algorithm to segment and extract features of painting works, and then uses the existing graphics and image software to simulate the nature of ink painting and its diffusion, and makes a series of transformation synthesis according to the existing camera photos to simulate and transform the color, line, texture, edge diffusion and other effects of the picture. Finally, the above results are applied to the art creation of traditional painting, which makes the original style of traditional painting more realistic. The experiment shows that using machine learning method to create traditional painting can realize the unity of authenticity and artistically. © 2023 IEEE.
Keyword :
Cluster analysis Cluster analysis Image segmentation Image segmentation K-means clustering K-means clustering Learning algorithms Learning algorithms Painting Painting Textures Textures
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GB/T 7714 | Wang, Weiqi . Application of K-Mean Clustering Algorithm in the Creation of Painting Art [C] . 2023 : 1-5 . |
MLA | Wang, Weiqi . "Application of K-Mean Clustering Algorithm in the Creation of Painting Art" . (2023) : 1-5 . |
APA | Wang, Weiqi . Application of K-Mean Clustering Algorithm in the Creation of Painting Art . (2023) : 1-5 . |
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In order to further improve the artistic style transfer effect of traditional painting, this paper proposes an artistic style transfer method of Chinese painting based on generative adversarial network. Where, the cyclic generative adversarial network (CycleGAN) is selected as the basic style transfer algorithm, and the loss function in the network is improved, so as to further enhance the style transfer effect. The experimental results show that compared with the CycleGAN before improvement, the improved CycleGAN has better stability. Compared with other transfer algorithms, the designed Chinese painting artistic style transfer algorithm based on improved CycleGAN has better transfer effect, and the FID score, PSNR and SSIM of the designed algorithm are 162.09, 99.61 and 0.7, respectively. In conclusion, the transfer effect of the designed style transfer algorithm is good, and the designed style transfer algorithm can be applied to the actual Chinese painting artistic style transfer with high reliability. © 2023 IEEE.
Keyword :
Generative adversarial networks Generative adversarial networks
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GB/T 7714 | Wang, Weiqi , Huang, Yi , Miao, Han . Research on Artistic Style Transfer of Chinese Painting Based on Generative Adversarial Network [C] . 2023 : 986-991 . |
MLA | Wang, Weiqi 等. "Research on Artistic Style Transfer of Chinese Painting Based on Generative Adversarial Network" . (2023) : 986-991 . |
APA | Wang, Weiqi , Huang, Yi , Miao, Han . Research on Artistic Style Transfer of Chinese Painting Based on Generative Adversarial Network . (2023) : 986-991 . |
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