Home > Papers

 
 
Procedural Texture Generation Based on Semantic Descriptions
DONG Jun-Yu 1, WANG Li-Na 1, LIU Jun 2,SUN Xin 1
1. Department of Computer Science and Technology, Ocean University of China, Qingdao 266100
2. School of Science and Information Science, Qingdao 266100
*Correspondence author
#Submitted by
Subject:
Funding: Specialized Research Fund for the Doctoral Program of Higher Education （No.No.20120132110018）, National Natural Science Foundation of China （No.No.61271405）
Opened online:17 May 2016
Accepted by: none
Citation: DONG Jun-Yu, WANG Li-Na, LIU Jun.Procedural Texture Generation Based on Semantic Descriptions[OL]. [17 May 2016] http://en.paper.edu.cn/en_releasepaper/content/4688895
 
 
Procedural textures are normally generated from mathematical models and have been widely used in computer games and animations for efficient rendering of natural elements, such as wood, marble, stone and other materials. Although the intuitive way to describe procedural texture is to use semantic attributes, there is no connection between procedural models, model parameters and texture semantic descriptions. In this paper, we propose a novel framework for generating procedural textures according to semantic descriptions. First a vocabulary of semantic attributes is collected for describing procedural textures based on extensive psychophysical experiments. Then a multi-label learning method is employed to label more new textures using the semantic attributes. We construct a procedural texture dataset with semantic attributes and further learn a low-dimensional semantic texture space. Finally, for a set of input semantic descriptions, we are able to find a generation model with proper parameters in this space. This model can be used to generate procedural textures that retain the input semantic attributes. Experimental results show that the proposed framework is effective and the generated procedural textures are correlated with the corresponding input semantic descriptions.
Keywords:Procedural texture; Semantic attributes; Generation; Multi-label learning
 
 
 

For this paper
  • ● PDF (0B)
  • ● Revision 0 　　
  • ● Print this paper
  • ● Recommend this paper to a friend
  • ● Add to my favorite list

    Saved Papers

    Please enter a name for this paper to be shown in your personalized Saved Papers list

Tags
Add yours

Related Papers

Statistics
PDF Downloaded 43
Bookmarked 0
Recommend 0
Comments Array
Submit your papers