“Automated video looping with progressive dynamism” by Liao, Joshi and Hoppe

  • ©Zicheng Liao, Neel Joshi, and Hugues Hoppe




    Automated video looping with progressive dynamism

Session/Category Title: Video & Warping




    Given a short video we create a representation that captures a spectrum of looping videos with varying levels of dynamism, ranging from a static image to a highly animated loop. In such a progressively dynamic video, scene liveliness can be adjusted interactively using a slider control. Applications include background images and slideshows, where the desired level of activity may depend on personal taste or mood. The representation also provides a segmentation of the scene into independently looping regions, enabling interactive local adjustment over dynamism. For a landscape scene, this control might correspond to selective animation and deanimation of grass motion, water ripples, and swaying trees. Converting arbitrary video to looping content is a challenging research problem. Unlike prior work, we explore an optimization in which each pixel automatically determines its own looping period. The resulting nested segmentation of static and dynamic scene regions forms an extremely compact representation.


    1. Agarwala, A., Dontcheva, M., Agrawala, M., Drucker, S., Colburn, A., Curless, B., Salesin, D., and Cohen, M. 2004. Interactive digital photomontage. ACM Trans. Graph., 23 (3):294–302. Google ScholarDigital Library
    2. Agarwala, A., Zheng, K. C., Pal, C., Agrawala, M., Cohen, M., Curless, B., Salesin, D., and Szeliski, R. 2005. Panoramic video textures. ACM Trans. Graph., 24(3). Google ScholarDigital Library
    3. Bai, J., Agarwala, A., Agrawala, M., and Ramamoorthi, R. 2012. Selectively de-animating video. ACM Trans. Graph., 31(4). Google ScholarDigital Library
    4. Beck, J. and Burg, K. 2012. Cinemagraphs. http://cinemagraphs.com/.Google Scholar
    5. Bennett, E. P. and McMillan, L. 2007. Computational time-lapse video. ACM Trans. Graph., 26(3). Google ScholarDigital Library
    6. Boykov, Y., Veksler, O., and Zabih, R. 2001. Fast approximate energy minimization via graph cuts. IEEE Trans. on Pattern Anal. Mach. Intell., 23(11). Google ScholarDigital Library
    7. Burt, P. J. and Adelson, E. H. 1983. A multiresolution spline with application to image mosaics. ACM Trans. Graph., 2(4). Google ScholarDigital Library
    8. Cho, T. S., Joshi, N., Zitnick, C. L., Kang, S. B., Szeliski, R., and Freeman, W. T. 2010. A content-aware image prior. In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR).Google Scholar
    9. Chuang, Y.-Y., Goldman, D. B., Zheng, K. C., Curless, B., Salesin, D. H., and Szeliski, R. 2005. Animating pictures with stochastic motion textures. ACM Trans. Graph., 24(3). Google ScholarDigital Library
    10. Cohen, M. and Szeliski, R. 2006. The moment camera. IEEE Computer, 39(8). Google ScholarDigital Library
    11. Couture, V., Langer, M., and Roy, S. 2011. Panoramic stereo video textures. ICCV, pages 1251–1258. Google ScholarDigital Library
    12. Freeman, W. T., Adelson, E. H., and Heeger, D. J. 1991. Motion without movement. ACM SIGGRAPH Proceedings. Google ScholarDigital Library
    13. Horn, B. K. P. and Schunk, B. G. 1981. Determining optical flow. Artificial Intelligence, 17:185–203.Google ScholarDigital Library
    14. Joshi, N., Mehta, S., Drucker, S., Stollnitz, E., Hoppe, H., Uyttendaele, M., and Cohen, M. 2012. Cliplets: Juxtaposing still and dynamic imagery. Proceedings of UIST. Google ScholarDigital Library
    15. Kolmogorov, V. and Zabih, R. 2004. What energy functions can be minimized via graph cuts? IEEE Trans. on Pattern Anal. Mach. Intell., 26(2). Google ScholarDigital Library
    16. Kwatra, V., Schödl, A., Essa, I., Turk, G., and Bobick, A. 2003. Graphcut textures: image and video synthesis using graph cuts. ACM Trans. Graph., 22(3):277–286. Google ScholarDigital Library
    17. Liu, C., Torralba, A., Freeman, W. T., Durand, F., and Adelson, E. H. 2005. Motion magnification. ACM Trans. Graph., 24(3):519–526. Google ScholarDigital Library
    18. Mahajan, D., Huang, F.-C., Matusik, W., Ramamoorthi, R., and Belhumeur, P. 2009. Moving gradients: A path-based method for plausible image interpolation. ACM Trans. Graph., 28(3):42. Google ScholarDigital Library
    19. Marks, J., Andalman, B., Beardsley, P. A., Freeman, W., Gibson, S., Hodgins, J., Kang, T., Mirtich, B., Pfister, H., Ruml, W., Ryall, K., Seims, J., and Shieber, S. 1997. Design galleries: A general approach to setting parameters for computer graphics and animation. ACM SIGGRAPH Proceedings. Google ScholarDigital Library
    20. Pritch, Y., Rav-Acha, A., and Peleg, S. 2008. Nonchronological video synopsis and indexing. IEEE Trans. on Pattern Anal. Mach. Intell., 30(11). Google ScholarDigital Library
    21. Rav-Acha, A., Pritch, Y., Lischinski, D., and Peleg, S. 2007. Dynamosaicing: Mosaicing of dynamic scenes. IEEE Trans. on Pattern Anal. Mach. Intell., 29(10). Google ScholarDigital Library
    22. Schödl, A., Szeliski, R., Salesin, D. H., and Essa, I. 2000. Video textures. In SIGGRAPH Proceedings, pages 489–498. Google ScholarDigital Library
    23. Sunkavalli, K., Matusik, W., Pfister, H., and Rusinkiewicz, S. 2007. Factored time-lapse video. ACM Trans. Graph., 26(3). Google ScholarDigital Library
    24. Tompkin, J., Pece, F., Subr, K., and Kautz, J. 2011. Towards moment images: Automatic cinemagraphs. In Proc. of the 8th European Conference on Visual Media Production (CVMP 2011). Google ScholarDigital Library
    25. Wang, J., Bhat, P., Colburn, R. A., Agrawala, M., and Cohen, M. F. 2005. Interactive video cutout. ACM Trans. Graph., 24(3). Google ScholarDigital Library
    26. Wu, H.-Y., Rubinstein, M., Shih, E., Guttag, J., Durand, F., and Freeman, W. 2012. Eulerian video magnification for revealing subtle changes in the world. ACM Trans. Graph., 31(4). Google ScholarDigital Library

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