Stroke-based stylization learning and rendering with inverse reinforcement learning
Authors: Xie, N., Zhao, T., Tian, F., Zhang, X. and Sugiyama, M.
Journal: IJCAI International Joint Conference on Artificial Intelligence
Volume: 2015-January
Pages: 2531-2539
ISBN: 9781577357384
ISSN: 1045-0823
Abstract:Among various traditional art forms, brush stroke drawing is one of the widely used styles in modern computer graphic tools such as GIMP, Photoshop and Painter. In this paper, we develop an AI-aided art authoring (A4) system of non-photorealistic rendering that allows users to automatically generate brush stroke paintings in a specific artist's style. Within the reinforcement learning framework of brush stroke generation proposed by Xie et al. [Xie et al., 2012], our contribution in this paper is to learn artists' drawing styles from video-captured stroke data by inverse reinforcement learning. Through experiments, we demonstrate that our system can successfully learn artists' styles and render pictures with consistent and smooth brush strokes.
https://eprints.bournemouth.ac.uk/22693/
Source: Scopus
Stroke-Based Stylization Learning and Rendering with Inverse Reinforcement Learning
Authors: Xie, N., Zhao, T., Tian, F., Zhang, X. and Sugiyama, M.
Journal: PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI)
Pages: 2531-2537
https://eprints.bournemouth.ac.uk/22693/
Source: Web of Science (Lite)
Stroke-Based Stylization Learning and Rendering with Inverse Reinforcement Learning
Authors: Xie, N., Zhao, T., Tian, F., Zhang, X.H. and Sugiyam, M.
Conference: International Joint Conference on Artificial Intelligence
Dates: 25-31 July 2015
https://eprints.bournemouth.ac.uk/22693/
Source: Manual
Stroke-Based Stylization Learning and Rendering with Inverse Reinforcement Learning
Authors: Xie, N., Zhao, T., Tian, F., Zhang, X.H. and Sugiyam, M.
Conference: International Joint Conference on Artificial Intelligence
Abstract:Among various traditional art forms, brush stroke drawing is one of the widely used styles in modern computer graphic tools such as GIMP, Photoshop and Painter. In this paper, we develop an AI-aided art authoring (A4) system of non- photorealistic rendering that allows users to automatically generate brush stroke paintings in a specific artist’s style. Within the reinforcement learning framework of brush stroke generation proposed by Xie et al.[Xie et al., 2012], our contribution in this paper is to learn artists’ drawing styles from video-captured stroke data by inverse reinforcement learning. Through experiments, we demonstrate that our system can successfully learn artists’ styles and render pictures with consistent and smooth brush strokes.
https://eprints.bournemouth.ac.uk/22693/
http://ijcai.org/papers15/Papers/IJCAI15-359.pdf
Source: BURO EPrints