Skip to main content

Narrative Generation by Repurposing Digital Videos

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6523))

Abstract

Storytelling and narrative creation are very popular research issues in the field of interactive media design. In this paper, we propose a framework for generating video narrative from existing videos which user only needs to involve in two steps: (1) select background video and avatars; (2) set up the movement and trajectory of avatars. To generate a realistic video narrative, several important steps have to be implemented. First, a video scene generation process is designed to generate a video mosaic. This video mosaic can be used as a basis for narrative planning. Second, an avatar preprocessing procedure with moderate avatar control technologies is designed to regulate a number of specific properties, such as the size or the length of constituent motion clips, and control the motion of avatars. Third, a layer merging algorithm and a spatiotemporal replacement algorithm are developed to ensure the visual quality of a generated video narrative. To demonstrate the efficacy of the proposed method, we generated several realistic video narratives from some chosen video clips and the results turned out to be visually pleasing.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   99.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Burt, P.J., Adelson, A.H.: A Multiresolution Spline With Application to Image Mosaics. ACM Trans. on Graphics 2, 217–236 (1983)

    Article  Google Scholar 

  2. Chen, T., Cheng, M.M., Tan, P., Shamir, A., Hu1, S.-M.: Sketch2Photo: Internet Image Montage. In: ACM SIGGRAPH ASIA (2009)

    Google Scholar 

  3. Cheung, C.-H., Po, L.-M.: Novel cross-diamond-hexagonal search algorithms for fast block motion estimation. IEEE Trans. on Multimedia 7(1), 16–22 (2005)

    Article  Google Scholar 

  4. Comaniciu, D., Meer, P.: Mean Shift: A Robust Approach toward Feature Space Analysis. IEEE Trans. on Pattern Analysis and Machine Intelligence 24(5) (May 2002)

    Google Scholar 

  5. Criminisi, A.: Single-View Metrology: Algorithms and Applications. In: Van Gool, L. (ed.) DAGM 2002. LNCS, vol. 2449, pp. 224–239. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  6. Fischler, M., Bolles, R.: Random sample consensus: A paradigm for model fitting with application to image analysis and automated cartography. Communications of the ACM 24, 381–395 (1981)

    Article  MathSciNet  Google Scholar 

  7. Lalonde, J.F., Hoeim, D., Efros, A.A., Rother, C., Winn, J., Criminisi, A.: Photo Clip Art. ACM Transactions on Graphics (SIGGRAPH 2007) 26(3) (August 2007)

    Google Scholar 

  8. Levin, A., Lischinski, D., Weiss, Y.: A Closed Form Solution to Natural Image Matting. In: Int. Prof. Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 61–68 (June 2007)

    Google Scholar 

  9. Lowe, D.G.: Object recognition from local scale-invariant features. In: Int. Conf. on Computer Vision, pp. 1150–1157 (September 1999)

    Google Scholar 

  10. Perez, P., Gangnet, M., Blake, A.: Poisson image editing. In: Proc. of ACM SIGGRAPH, pp. 313–318 (2003)

    Google Scholar 

  11. Reinhard, E., Adhikhmin, M., Gooch, B., Shirley, P.: Color transfer between images. In: Proc. Conf. on IEEE Computer Graphics and Applications, vol. 21(5), pp. 34–41 (2001)

    Google Scholar 

  12. Shih, T.K., Tang, N.C., Tsai, J.C., Zhong, H.-Y.: Video Falsifying by Motion Interpolation and Inpainting. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition (2008)

    Google Scholar 

  13. Shih, T.K., Tang, N.C., Hwang, J.-N.: Exemplar-based Video Inpainting without Ghost Shadow Artifacts by Maintaining Temporal Continuity. IEEE Trans. on Circuits and Systems for Video Technology 19(2) (March 2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Tang, N.C., Tyan, HR., Hsu, CT., Liao, HY.M. (2011). Narrative Generation by Repurposing Digital Videos. In: Lee, KT., Tsai, WH., Liao, HY.M., Chen, T., Hsieh, JW., Tseng, CC. (eds) Advances in Multimedia Modeling. MMM 2011. Lecture Notes in Computer Science, vol 6523. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17832-0_47

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-17832-0_47

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17831-3

  • Online ISBN: 978-3-642-17832-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics