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Computer-Assisted Repurposing of Existing Animations

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Image and Video-Based Artistic Stylisation

Part of the book series: Computational Imaging and Vision ((CIVI,volume 42))

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Abstract

Despite the recent proliferation of modern 3D computer-generated imagery, the classical 2D hand-drawn style retains an important role in the field of cartoon animation. Although existing 3D modelling and animation tools open a vast pool of new possibilities, they still suffer from lack of expressiveness. This chapter presents a selection of advanced image processing techniques, the aim of which is to build a bridge between hand-drawn 2D animation and fully computer-assisted approaches. Tailored for usage in a real production pipeline, these techniques enable various complex manipulation and enhancement tasks such as colorization, 2D-to-3D conversion, example-based synthesis or rendering similar to 3D computer-generated imagery to be done with minimal user effort.

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Acknowledgements

This work has been supported by the Marie Curie action ERG, No. PERG07-GA-2010-268216 and partially by the Technology Agency of the Czech Republic under the project TE01010415 (V3C—Visual Computing Competence Center). Hand-drawn images used in this chapter are courtesy of UPP & DMP, Anifilm, Lukáš Vlček, and Ondřej Sýkora.

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Authors and Affiliations

  1. FEE, DCGI, CTU in Prague, Karlovo nám. 13, 121 35, Praha 2, Czech Republic

    Daniel Sýkora

  2. Trinity College Dublin, College Green, Dublin 2, Ireland

    John Dingliana

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  1. Daniel Sýkora
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  2. John Dingliana
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Correspondence to Daniel Sýkora .

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Editors and Affiliations

  1. School of Computer Science & Informatics, Cardiff University, The Parade, Roath 5, Cardiff, CF24 3AA, United Kingdom

    Paul Rosin

  2. Centre for Vision Speech & Signal Proc., University of Surrey, Guildford, GU2 7XH, Surrey, United Kingdom

    John Collomosse

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Sýkora, D., Dingliana, J. (2013). Computer-Assisted Repurposing of Existing Animations. In: Rosin, P., Collomosse, J. (eds) Image and Video-Based Artistic Stylisation. Computational Imaging and Vision, vol 42. Springer, London. https://doi.org/10.1007/978-1-4471-4519-6_14

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  • DOI: https://doi.org/10.1007/978-1-4471-4519-6_14

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  • Print ISBN: 978-1-4471-4518-9

  • Online ISBN: 978-1-4471-4519-6

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