Aesthetic style transferring method based on deep neural network between Chinese landscape painting and classical private garden’s virtual scenario
Most of the existing virtual scenarios built for the digital protection of Chinese classical private gardens are too modern in expression style to show the aesthetic significance of their historical period. Considering the aesthetic commonality between traditional Chinese landscape paintings and cla...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2023-12-01
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| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/17538947.2023.2202422 |
| _version_ | 1797678512596844544 |
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| author | Shuai Hong Jie Shen Guonian Lü Xiaoyan Liu Yirui Mao Nina Sun Long Tang |
| author_facet | Shuai Hong Jie Shen Guonian Lü Xiaoyan Liu Yirui Mao Nina Sun Long Tang |
| author_sort | Shuai Hong |
| collection | DOAJ |
| description | Most of the existing virtual scenarios built for the digital protection of Chinese classical private gardens are too modern in expression style to show the aesthetic significance of their historical period. Considering the aesthetic commonality between traditional Chinese landscape paintings and classical private gardens and referring to image style transfer, here, a deep neural network was proposed to transfer the aesthetic style from landscape paintings to the virtual scenario of classical private gardens. The network consisted of two parts: style prediction and style transfer. The style prediction network was used to obtain style representation from style paintings, and the style transfer network was used to transfer style representation to the content scenario. The pre-trained network was then embedded into the scenario rendering pipeline and combined with the screen post-processing method to realise the stylised expression of the virtual scenario. To verify the feasibility of this methodology, a virtual scenario of the Humble Administrator’s Garden was used as the content scenario and five garden landscape paintings from different time periods and painting styles were selected for the case study. The results demonstrated that this methodology could effectively achieve the aesthetic style transfer of a virtual scenario. |
| first_indexed | 2024-03-11T23:00:52Z |
| format | Article |
| id | doaj.art-c221f6b8ddd347f0a276fcaa0c327c2a |
| institution | Directory Open Access Journal |
| issn | 1753-8947 1753-8955 |
| language | English |
| last_indexed | 2024-03-11T23:00:52Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj.art-c221f6b8ddd347f0a276fcaa0c327c2a2023-09-21T14:57:12ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552023-12-011611491150910.1080/17538947.2023.22024222202422Aesthetic style transferring method based on deep neural network between Chinese landscape painting and classical private garden’s virtual scenarioShuai Hong0Jie Shen1Guonian Lü2Xiaoyan Liu3Yirui Mao4Nina Sun5Long Tang6Nanjing Normal UniversityNanjing Normal UniversityNanjing Normal UniversityNanjing Normal UniversityZhejiang UniversityJiangsu Second Normal UniversityNanjing Normal UniversityMost of the existing virtual scenarios built for the digital protection of Chinese classical private gardens are too modern in expression style to show the aesthetic significance of their historical period. Considering the aesthetic commonality between traditional Chinese landscape paintings and classical private gardens and referring to image style transfer, here, a deep neural network was proposed to transfer the aesthetic style from landscape paintings to the virtual scenario of classical private gardens. The network consisted of two parts: style prediction and style transfer. The style prediction network was used to obtain style representation from style paintings, and the style transfer network was used to transfer style representation to the content scenario. The pre-trained network was then embedded into the scenario rendering pipeline and combined with the screen post-processing method to realise the stylised expression of the virtual scenario. To verify the feasibility of this methodology, a virtual scenario of the Humble Administrator’s Garden was used as the content scenario and five garden landscape paintings from different time periods and painting styles were selected for the case study. The results demonstrated that this methodology could effectively achieve the aesthetic style transfer of a virtual scenario.http://dx.doi.org/10.1080/17538947.2023.2202422chinese classical private gardenvirtual scenariochinese traditional landscape paintingdeep neural networkaesthetic style transfer |
| spellingShingle | Shuai Hong Jie Shen Guonian Lü Xiaoyan Liu Yirui Mao Nina Sun Long Tang Aesthetic style transferring method based on deep neural network between Chinese landscape painting and classical private garden’s virtual scenario International Journal of Digital Earth chinese classical private garden virtual scenario chinese traditional landscape painting deep neural network aesthetic style transfer |
| title | Aesthetic style transferring method based on deep neural network between Chinese landscape painting and classical private garden’s virtual scenario |
| title_full | Aesthetic style transferring method based on deep neural network between Chinese landscape painting and classical private garden’s virtual scenario |
| title_fullStr | Aesthetic style transferring method based on deep neural network between Chinese landscape painting and classical private garden’s virtual scenario |
| title_full_unstemmed | Aesthetic style transferring method based on deep neural network between Chinese landscape painting and classical private garden’s virtual scenario |
| title_short | Aesthetic style transferring method based on deep neural network between Chinese landscape painting and classical private garden’s virtual scenario |
| title_sort | aesthetic style transferring method based on deep neural network between chinese landscape painting and classical private garden s virtual scenario |
| topic | chinese classical private garden virtual scenario chinese traditional landscape painting deep neural network aesthetic style transfer |
| url | http://dx.doi.org/10.1080/17538947.2023.2202422 |
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