Skip to main content
SpringerLink
Log in

Pointillist video stylization based on particle tracing

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

We present an algorithm that stylizes an input video into a painterly animation without user intervention. In particular, we focus on pointillist animation with stable temporal coherence. Temporal coherence is an important problem in non-photorealistic rendering for videos. To realize pointillist animation, the various characters of pointillism should be considered in painting process to maintain temporal coherence. For this, weused the particle video algorithm which is a new approach to long-range motion estimation in video. Based on this method, we introduce a method to control the density of particles considering the features of frames and importance maps. Finally, the propagation methods of stroke to minimize flickering effects of brush strokes are introduced.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Gossett N, Chen B (2004) Paint inspired color mixing and compositing for visualization. In: INFOVIS ’04: Proceedings of the IEEE symposium on information visualization, pp 113–118

  2. Haeberli P (1990) Paint by numbers: Abstract image representations. ACM SIGGRAPH Comput Graph 24(4):207–214. doi:10.1145/97880.97902

    Article  Google Scholar 

  3. Hays J, Essa I (2004) Image and video based painterly animation. In: Proc. NPAR’04, pp 113–120

  4. Hertzmann A (1998) Painterly rendering with curved brush strokes of multiple sizes. In: Proc. SIGGRAPH’98, pp 453–460

  5. Hertzmann A (2001) Paint by relaxation. In: Computer graphics international’01, pp 47–54

  6. Hertzmann A, Perlin K (2000) Painterly rendering for video and interaction. In: Proceedings of the 1st international symposium on non-photorealistic animation and rendering, NPAR ’00. ACM, New York, pp 7–12

    Chapter  Google Scholar 

  7. Hua Huang LZ, Fu TN (2010) Video painting via motion layer manipulation. Comput Graph Forum 29(7):2055–2064

    Article  Google Scholar 

  8. Kagaya M, Brendel W, Deng Q, Kesterson T, Todorovic S, Neill PJ, Zhang E (2011) Video painting with space-time-varying style parameters. IEEE Trans Vis Comput Graph 17(1):74–87. doi:10.1109/TVCG.2010.25

    Article  Google Scholar 

  9. Kang H, Lee S, Chui CK (2007) Coherent line drawing. In: In Proc. non-photorealistic animation and rendering, pp 43–50

  10. Lee H, Seo S, Ryoo S, Ahn K, Yoon K (2012) A multi-level depiction method for painterly rendering based on visual perception cue. Multimed Tools Appl 1–16. doi:10.1007/s11042-012-1036-x

    Google Scholar 

  11. Lin L, Zeng K, Lv H, Wang Y, Xu Y, Zhu SC (2010) Painterly animation using video semantics and feature correspondence. In: Proceedings of the 8th international symposium on non-photorealistic animation and rendering, NPAR ’10. ACM, New York, pp 73–80

    Google Scholar 

  12. Litwinowicz PC (1997) Processing images and video for an impressionist effect. In: Proc. SIGGRAPH’97, pp 407–414

  13. Meier BJ (1996) Painterly rendering for animation. In: In SIGGRAPH 96 conference proceedings, pp 477–484

  14. O’Donovan P, Hertzmann A (2012) Anipaint: interactive painterly animation from video. IEEE Trans Vis Comput Graph 18(3):475–487. doi:10.1109/TVCG.2011.51

    Article  Google Scholar 

  15. Park Y, Yoon K (2008) Painterly animation using motion maps. Graph Models 70(1–2):1–15

    Article  MathSciNet  Google Scholar 

  16. Sand P, Teller S (2008) Particle video: Long-range motion estimation using point trajectories. Int J Comput Vis 80(1):72–91. doi:10.1007/s11263-008-0136-6

    Article  Google Scholar 

  17. Schwarz M, Stamminger M (2009) On predicting visual popping in dynamic scenes. In: Proceedings of the 6th symposium on applied perception in graphics and visualization, APGV ’09. ACM, New York, pp 93–100. doi:10.1145/1620993.1621012

    Chapter  Google Scholar 

  18. Seo S, Yoon K (2010) Color juxtaposition for pointillism based on an artistic color model and a statistical analysis. Vis Comput 26(6–8):421–431

    Article  Google Scholar 

  19. Seo S, Ryoo S, Park J (2011) Interactive painterly rendering with artistic error correction. Multimed Tools Appl 1–17. doi:10.1007/s11042-011-0796-z

    Google Scholar 

  20. Yantis S, Jonides J (1984) Abrupt visual onsets and selective attention: evidence from visual search. J Exp Psychol Hum Percept Perform 10:601–621

    Article  Google Scholar 

  21. Zhao M, Zhu SC (2011) Customizing painterly rendering styles using stroke processes. In: Collomosse JP, Asente P, Spencer SN (eds) NPAR. ACM, New York, pp 137–146

    Google Scholar 

Download references

Acknowledgements

This work was supported by the Korea Research Foundation Grant funded by the Korean Government (KRF-2011-357-D00202) and was partly supported by French institutional grant AMCQMCSGA ANR-10-CEXC-002.

Author information

Authors and Affiliations

  1. LIRIS, Lyon 1 University, Lyon, France

    SangHyun Seo & Victor Ostromoukhov

Authors
  1. SangHyun Seo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Victor Ostromoukhov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to SangHyun Seo.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Seo, S., Ostromoukhov, V. Pointillist video stylization based on particle tracing. Multimed Tools Appl 71, 279–292 (2014). https://doi.org/10.1007/s11042-013-1441-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-013-1441-9

Keywords

Search

Navigation