Skip to main content
Log in

Creating waterfall animation on a single image

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

Abstract

A static image always becomes more eye-catching with an animation. In this paper, we present a system for adding a waterfall animation to a single image by extracting a flow animation from a video sequence. To the best of our knowledge, this is the first attempt by researchers to create a waterfall animation on a still waterfall image. Such work poses challenges in many areas, including color consistency, texture consistency, flow velocity, block matching, and block effects. The proposed method integrates optical flow, line integral convolution, color transfer, graph-cut, and multi-resolution splining techniques to mimic a real waterfall on a single image. It uses a segmentation process to separate the necessary foreground and the unnecessary background. Then, flow analysis is performed on the target image and source video. Finally, flow similarity and a synthesis process are applied to form the animation. Experiments generated 8 animation results that prove the feasibility of the proposed method. The limitations and potential impact of this research are also discussed in our experimental results.

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

Access this article

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
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

References

  1. Bai J, Agarwala A, Agrawala M, Ramamoorthi R (2013) Automatic cinemagraph portraits. Computer Graphics Forum 32(4):17–25

    Article  Google Scholar 

  2. Barrett WA, Cheney AS (2002) Object-based image editing. ACM Trans Graph 21(3):777–784

    Article  Google Scholar 

  3. Bhat KS, Seitz SM, Hodgins JK, Khosla PK (2004) Flow-based video synthesis and editing. ACM Trans Graph 23(3):360–363

    Article  Google Scholar 

  4. Burt PJ, Adelson EH (1983) A multiresolution spline with application to image mosaics. ACM Trans Graph 2(4):217–236

    Article  Google Scholar 

  5. Cabral B, Leedom LC (1993) Imaging vector fields using line integral convolution, in Proceedings of the 20th Annual Conference on Computer Graphics and Interactive Techniques, pp. 263–270

  6. Chuang YY, Goldman DB, Zheng KC, Curless B, Salesin DH, Szeliski R (2005) Animating pictures with stochastic motion textures. ACM Trans Graph 24(3):853–860

    Article  Google Scholar 

  7. Efros AA, Freeman WT (2001) Image quilting for texture synthesis and transfer, in Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques: ACM, pp. 341–346

  8. Farnebäck G (2003) Two-frame motion estimation based on polynomial expansion, in Scandinavian Conference on Image Analysis: Springer, Berlin, Heidelberg, pp. 363–370

    Chapter  Google Scholar 

  9. Gonzalex RC, Woods RE (2006) Digital image processing (3ed ed.). Prentice-Hall, Inc., Upper Saddle River

    Google Scholar 

  10. He K, Rhemann C, Rother C, Tang X, Sun J (2011) A global sampling method for alpha matting, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2049–2056

  11. Hornung A, Dekkers E, Kobbelt L (2007) Character animation from 2D pictures and 3D motion data. ACM Trans Graph 26(1):1

    Article  Google Scholar 

  12. Igarashi T, Moscovich T, Hughes JF (2005) As-rigid-as-possible shape manipulation. ACM Trans Graph 24(3):1134–1141

    Article  Google Scholar 

  13. Kwatra V, Schödl A, Essa I, Turk G, Bobick A (2003) Graphcut textures: image and video synthesis using graph cuts. ACM Trans Graph 22(3):277–286

    Article  Google Scholar 

  14. Litwinowicz P, Williams L (1994) Animating images with drawings, in Proceedings of the 21st Annual Conference on Computer Graphics and Interactive Techniques: ACM Press/Addison-Wesley Publishing Co., pp. 409–412

  15. Liu Y, Nie L, Han L, Zhang L, Rosenblum DS (2015) Action2Activity: recognizing complex activities from sensor data, in Proceedings of the 24th International Joint Conference on Artificial Intelligence, pp. 1617–1623

  16. Liu L, Cheng L, Liu Y, Jia Y, Rosenblum DS (2016) Recognizing complex activities by a probabilistic interval-based model, in AAAI Conference on Artificial Intelligence, pp. 1266–1272

  17. Liu Y, Nie L, Liu L, Rosenblum DS (2016) From action to activity: sensor-based activity recognition. Neurocomputing 118(1):108–115

    Article  Google Scholar 

  18. Liu Y, Zhang L, Nie L, Yan Y, Rosenblum DS (2016) Fortune teller: predicting your career path, in AAAI Conference on Artificial Intelligence, pp. 201–207

  19. McNamara A, Treuille A, Popovic Z, Stam J (2004) Fluid control using the adjoint method. ACM Trans Graph 23(3):449–456

    Article  Google Scholar 

  20. Porter T, Duff T (1984) Compositing digital images. ACM Siggraph Computer Graphics 18(3):253–259

    Article  Google Scholar 

  21. Reinhard E, Ashikhmin M, Gooch B, Shirley P (2001) Color transfer between images. IEEE Comput Graph Appl 21(5):34–41

    Article  Google Scholar 

  22. Rother C, Kolmogorov V, Blake A (2004) Grabcut: interactive foreground extraction using iterated graph cuts. ACM Trans Graph 23(3):309–314

    Article  Google Scholar 

  23. Tompkin J, Pece F, Subr K, Kautz J (2011) Towards moment imagery: Automatic cinemagraphs, in: Proceedings of IEEE Conference on Visual Media Production, pp. 87–93

  24. Treuille A, McNamara A, Popovic Z, Stam J (2003) Keyframe control of smoke simulations. ACM Trans Graph 22(3):716–723

    Article  Google Scholar 

  25. Wang Y, Zhu S-C (2003) Modeling textured motion: particle, wave and sketch, in Proceedings. Ninth IEEE International Conference on Computer Vision, pp. 213–220

  26. Xie L, Zhu L, Chen G (2016) Unsupervised multi-graph cross-modal hashing for large-scale multimedia retrieval. Multimedia Tools and Applications 75(15):9185–9204

    Article  Google Scholar 

  27. Zhu L, Jin H, Zheng R, Feng X (2014) Effective naive Bayes nearest neighbor based image classification on GPU. J Supercomput 68(2):820–848

    Article  Google Scholar 

  28. Zhu L, Shen J, Xie L (2015) Topic hypergraph hashing for mobile image retrieval, in: Proceedings of the 23rd ACM International Conference on Multimedia, pp. 843–846

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Timothy K. Shih.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lin, CY., Huang, YW. & Shih, T.K. Creating waterfall animation on a single image. Multimed Tools Appl 78, 6637–6653 (2019). https://doi.org/10.1007/s11042-018-6332-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6332-7

Keywords

Navigation