Abstract
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 [1], Photoshop [2], and Meitu [3], 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.
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References
https://en.wikipedia.org/w/index.php?title=Prisma_(app)&oldid=769699619
https://en.wikipedia.org/w/index.php?title=Adobe_Creative_Cloud&oldid=771515328
https://en.wikipedia.org/w/index.php?title=Meitu&oldid=761855434
https://en.wikipedia.org/w/index.php?title=Slack_(software)&oldid=771140361
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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Project 61602088, Project 61572108 and Project 61632007 and the Fundamental Research Funds for the Central Universities under Project ZYGX2016J212, Project ZYGX2015J055 and Project ZYGX2014Z007. Authors are grateful to the anonymous reviewers for their careful readings and valuable feedback.
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Xie, N., Ren, M., Yang, W., Yang, Y., Shen, H.T. (2017). WebPainter: Collaborative Stroke-Based Rendering Through HTML5 and WebGL. In: Tian, F., Gatzidis, C., El Rhalibi, A., Tang, W., Charles, F. (eds) E-Learning and Games. Edutainment 2017. Lecture Notes in Computer Science(), vol 10345. Springer, Cham. https://doi.org/10.1007/978-3-319-65849-0_22
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DOI: https://doi.org/10.1007/978-3-319-65849-0_22
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