Cited By
View all- Zhang M(2022)Real-Time Generation Method of Oil Painting Style Brushstrokes Based on Inverse Reinforcement LearningMobile Information Systems10.1155/2022/29969602022Online publication date: 1-Jan-2022
Web-based SBLR method of multimedia tools for computer-aided drawing
Pages 799 - 816
Abstract
As one of the most successful multimedia tools for digital media and creative industry, computer-aided drawing system assists users to convert the input real photos into painterly style images. Nowadays, it is widely developed as cloud brush engine service in many creative software tools and applications of artistic rendering such as Prisma, Photoshop Cloud, and Meitu, because the machine learning server has more powerful than the stand-alone version. In this paper, we propose a web collaborative Stroke-based Learning and Rendering (WebSBLR) system. Different from the existing methods that are mainly focused on the artistic filters, we concentrate on the stroke realistic rendering engine for browser on client using WebGL and HTML5. Moreover, we implement the learning-based stroke drawing path generation module on the server. By this way, we enable to achieve the computer-supported cooperative work (CSCW), especially for multi-screen synchronous interaction. The experiments demonstrated our method are efficient to web-based multi-screen painting simulation.It can successfully learn artists' styles and render pictures with consistent and smooth brush strokes.
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Published: 01 January 2019
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Cited By
View all- Zhang M(2022)Real-Time Generation Method of Oil Painting Style Brushstrokes Based on Inverse Reinforcement LearningMobile Information Systems10.1155/2022/29969602022Online publication date: 1-Jan-2022