skip to main content
research-article

Selectively de-animating video

Published:01 July 2012Publication History
Skip Abstract Section

Abstract

We present a semi-automated technique for selectively deanimating video to remove the large-scale motions of one or more objects so that other motions are easier to see. The user draws strokes to indicate the regions of the video that should be immobilized, and our algorithm warps the video to remove the large-scale motion of these regions while leaving finer-scale, relative motions intact. However, such warps may introduce unnatural motions in previously motionless areas, such as background regions. We therefore use a graph-cut-based optimization to composite the warped video regions with still frames from the input video; we also optionally loop the output in a seamless manner. Our technique enables a number of applications such as clearer motion visualization, simpler creation of artistic cinemagraphs (photos that include looping motions in some regions), and new ways to edit appearance and complicated motion paths in video by manipulating a de-animated representation. We demonstrate the success of our technique with a number of motion visualizations, cinemagraphs and video editing examples created from a variety of short input videos, as well as visual and numerical comparison to previous techniques.

Skip Supplemental Material Section

Supplemental Material

tp167_12.mp4

References

  1. Agarwala, A., Dontcheva, M., Agrawala, M., Drucker, S., Colburn, A., Curless, B., Salesin, D., and Cohen, M. 2004. Interactive digital photomontage. ACM Transactions on Graphics 23, 3 (Aug.), 294--302. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Agarwala, A., Zheng, K. C., Pal, C., Agrawala, M., Cohen, M., Curless, B., Salesin, D. H., and Szeliski, R. 2005. Panoramic video textures. ACM Transactions on Graphics 24, 3 (Aug.), 821--827. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Assa, J., Caspi, Y., and Cohen-Or, D. 2005. Action synopsis: pose selection and illustration. ACM Transactions on Graphics 24, 3 (Aug.), 667--676. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Bai, X., Wang, J., Simons, D., and Sapiro, G. 2009. Video snapcut: Robust video object cutout using localized classifiers. ACM Transactions on Graphics 28, 3 (July), 70:1--70:11. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Barnes, C., Goldman, D. B., Shechtman, E., and Finkelstein, A. 2010. Video tapestries with continuous temporal zoom. ACM Transactions on Graphics 29, 4 (July), 89:1--89:9. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Bennett, E. P., and McMillan, L. 2007. Computational time-lapse video. ACM Transactions on Graphics 26, 3 (July), 102:1--102:6. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Boykov, Y., Veksler, O., and Zabih, R. 2001. Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 11 (Nov.), 1222--1239. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Caspi, Y., Axelrod, A., Matsushita, Y., and Gamliel, A. 2006. Dynamic stills and clip trailers. The Visual Computer 22, 9--10, 642--652. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Chuang, Y.-Y., Agarwala, A., Curless, B., Salesin, D. H., and Szeliski, R. 2002. Video matting of complex scenes. ACM Transactions on Graphics 21, 3 (July), 243--248. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. 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 Transactions on Graphics 24, 3 (Aug.), 853--860. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Cohen, M. F., and Szeliski, R. 2006. The moment camera. Computer 39, 8, 40--45. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Correa, C. D., and Ma, K.-L. 2010. Dynamic video narratives. ACM Transactions on Graphics 29, 4 (July), 88:1--88:9. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Goldman, D. B., Curless, B., Salesin, D., and Seitz, S. M. 2006. Schematic storyboarding for video visualization and editing. ACM Transactions on Graphics 25, 3 (July), 862--871. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Kim, B., and Essa, I. 2005. Video-based nonphotorealistic and expressive illustration of motion. In Computer Graphics International 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Kwatra, V., Schödl, A., Essa, I., Turk, G., and Bobick, A. 2003. Graphcut textures: Image and video synthesis using graph cuts. ACM Transactions on Graphics 22, 3 (July), 277--286. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Liu, C., Torralba, A., Freeman, W. T., Durand, F., and Adelson, E. H. 2005. Motion magnification. ACM Transactions on Graphics 24, 3 (Aug.), 519--526. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Liu, F., Gleicher, M., Jin, H., and Agarwala, A. 2009. Content-preserving warps for 3d video stabilization. ACM Transactions on Graphics 28, 3 (July), 44:1--44:9. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Liu, F., Gleicher, M., Wang, J., Jin, H., and Agarwala, A. 2011. Subspace video stabilization. ACM Transactions on Graphics 30, 1 (Jan.), 4:1--4:10. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Lucas, B. D., and Kanade, T. 1981. An iterative image registration technique with an application to stereo vision. International Joint Conference on Artificial Intelligence. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Pritch, Y., Rav-Acha, A., and Peleg, S. 2008. Nonchronological video synopsis and indexing. IEEE Transactions on Pattern Analysis and Machine Intelligence 30, 11 (Nov.), 1971--1984. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Rav-Acha, A., Kohli, P., Rother, C., and Fitzgibbon, A. 2008. Unwrap mosaics: A new representation for video editing. ACM Transactions on Graphics 27, 3 (Aug.), 17:1--17:11. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Rubinstein, M., Liu, C., Sand, P., Durand, F., and Freeman, W. T. 2011. Motion denoising with application to time-lapse photography. IEEE Computer Vision and Pattern Recognition (CVPR) (June), 313--320. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Schödl, A., Szeliski, R., Salesin, D. H., and Essa, I. 2000. Video textures. In Proceedings of ACM SIGGRAPH 2000, Computer Graphics Proceedings, Annual Conference Series, 489--498. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Shi, J., and Tomasi, C. 1994. Good features to track. In Computer Vision and Pattern Recognition, 593--600.Google ScholarGoogle Scholar
  25. Sutton, G. P., and Burrows, M. 2011. Biomechanics of jumping in the flea. J Exp Biol 214, 5 (Mar.), 836--847.Google ScholarGoogle ScholarCross RefCross Ref
  26. Tompkin, J., Pece, F., Subr, K., and Kautz, J. 2011. Towards moment imagery: Automatic cinemagraphs. Visual Media Production, Conference for 0, 87--93. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Truong, B. T., and Venkatesh, S. 2007. Video abstraction: A systematic review and classification. ACM Trans. Multimedia Comput. Commun. Appl. 3, 1 (Feb.). Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Selectively de-animating video
    Index terms have been assigned to the content through auto-classification.

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in

    Full Access

    • Published in

      cover image ACM Transactions on Graphics
      ACM Transactions on Graphics  Volume 31, Issue 4
      July 2012
      935 pages
      ISSN:0730-0301
      EISSN:1557-7368
      DOI:10.1145/2185520
      Issue’s Table of Contents

      Copyright © 2012 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 1 July 2012
      Published in tog Volume 31, Issue 4

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader