标题：Perception-driven procedural texture generation from examples
作者：Liu, Jun; Gan, Yanhai; Dong, Junyu; Qi, Lin; Sun, Xin; Jian, Muwei; Wang, Lina; Yu, Hui
作者机构：[Liu, Jun] Qingdao Agr Univ, Sci & Informat Coll, 700 Changcheng Rd, Qingdao, Peoples R China.; [Gan, Yanhai] Hisense TransTech Co Ltd, 17 Donghai W 更多
通讯作者地址：[Dong, JY]Ocean Univ China, Dept Comp Sci & Technol, 238 Songling Rd, Qingdao, Peoples R China.
关键词：Procedural texture; Texture generation; Texture perception;; Convolutional neural network; Deep learning; PCA-based Convolutional; Network
摘要：Procedural textures are widely used in computer games and animations for efficiently rendering natural scenes. They are generated using mathematical functions, and users need to tune the model parameters to produce desired texture. However, unless one has a good knowledge of these procedural models, it is difficult to predict which model can produce what types of textures. This paper proposes a framework for generating new procedural textures from examples. The new texture can have the same perceptual attributes as those of the input example or re-defined by the users. To achieve this goal, we first introduce a PCA-based Convolutional Network (PCN) to effectively learn texture features. These PCN features can be used to accurately predict the perceptual scales of the input example and a procedural model that can generate the input. Perceptual scales of the input can be redefined by users and further mapped to a point in the perceptual texture space, which has been established in advance by using a training dataset. Finally, we determine the parameters of the procedural generation model by performing perceptual similarity measurement in the perceptual texture space. Extensive experiments show that our method has produced promising results. (c) 2018 Elsevier B.V. All right sreserved.