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...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 andfive 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.展开更多
Dunhuang murals are gems of Chinese traditional art. This paper demonstrates a simple, yet powerful method to automatically identify the aesthetic visual style that lies in Dunhuang murals. Based on the art knowledge ...Dunhuang murals are gems of Chinese traditional art. This paper demonstrates a simple, yet powerful method to automatically identify the aesthetic visual style that lies in Dunhuang murals. Based on the art knowledge on Dunhuang murals, the method explicitly predicts some of possible image attributes that a human might use to understand the aesthetic visual style of a mural. These cues fall into three broad types: ① composition attributes related to mural layout or configuration; ② color attributes related to color types depicted; ③ brightness attributes related to bright conditions. We show that a classifier trained on these attributes can provide an efficient way to predict the aesthetic visual style of Dunhuang murals.展开更多
基金This work was supported by the Key Project of the National Natural Science Foundation of China(NSFC)under Grant 41930104National Key R&D Program of China under Grant 2021 YFE0112300+1 种基金Postgraduate Research&Practice Innovation Program of Jiangsu Province under Grant KYCX21_1336China Scholarship Council under Grant 202206860019.
文摘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 andfive 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.
基金the National Basic Research Program(973)of China(No.2012CB725305)the National Key Technology R&D Program of China(No.2012BAH03F02)
文摘Dunhuang murals are gems of Chinese traditional art. This paper demonstrates a simple, yet powerful method to automatically identify the aesthetic visual style that lies in Dunhuang murals. Based on the art knowledge on Dunhuang murals, the method explicitly predicts some of possible image attributes that a human might use to understand the aesthetic visual style of a mural. These cues fall into three broad types: ① composition attributes related to mural layout or configuration; ② color attributes related to color types depicted; ③ brightness attributes related to bright conditions. We show that a classifier trained on these attributes can provide an efficient way to predict the aesthetic visual style of Dunhuang murals.
