skip to main content
research-article

Three-dimensional proxies for hand-drawn characters

Published:02 February 2012Publication History
Skip Abstract Section

Abstract

Drawing shapes by hand and manipulating computer-generated objects are the two dominant forms of animation. Though each medium has its own advantages, the techniques developed for one medium are not easily leveraged in the other medium because hand animation is two-dimensional, and inferring the third dimension is mathematically ambiguous. A second challenge is that the character is a consistent three-dimensional (3D) object in computer animation while hand animators introduce geometric inconsistencies in the two-dimensional (2D) shapes to better convey a character's emotional state and personality. In this work, we identify 3D proxies to connect hand-drawn animation and 3D computer animation. We present an integrated approach to generate three levels of 3D proxies: single-points, polygonal shapes, and a full joint hierarchy. We demonstrate how this approach enables one medium to take advantage of techniques developed for the other; for example, 3D physical simulation is used to create clothes for a hand-animated character, and a traditionally trained animator is able to influence the performance of a 3D character while drawing with paper and pencil.

Skip Supplemental Material Section

Supplemental Material

tp116_12.mp4

References

  1. Agarwal, A. and Triggs, B. 2006. Recovering 3d human pose from monocular images. IEEE Trans. Pattern Anal. Mach. Intell. 28, 1, 44--58. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Baran, I., Vlasic, D., Grinspun, E., and Popović, J. 2009. Semantic deformation transfer. ACM Trans. Graph. 28, 3, 1--6. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Bourdev, L. and Malik, J. 2009. Poselets: Body part detectors trained using 3d human pose annotations. In Proceedings of the IEEE International Conference on Computer Vision.Google ScholarGoogle Scholar
  4. Bregler, C. and Malik, J. 1998. Tracking people with twists and exponential maps. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Cooper, D. 2002. 2D/3D Hybrid character animation on Spirit. In Proceedings of the ACM SIGGRAPH '02 Conference Abstracts and Applications Conference. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Corrêa, W. T., Jensen, R. J., Thayer, C. E., and Finkelstein, A. 1998. Texture mapping for cel animation. In Proceedings of the ACM SIGGRAPH '98. 435--446. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Culhane, S. 1990. Animation From Script to Screen. St. Martin's Press, New York.Google ScholarGoogle Scholar
  8. Daniels, E. 1999. Deep canvas in Disney's Tarzan. In Proceedings of the ACM SIGGRAPH '99 Conference Abstracts and Applications. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Davis, J., Agrawala, M., Chuang, E., Popovic, Z., and Salesin, D. H. 2003. A sketching interface for articulated figure animation. In Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation. 320--328. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Demirdjian, D., Ko, T., and Darrel, T. 2003. Constraining human body tracking. In Proceedings of the IEEE International Conference on Computer Vision. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Elgammal, A. and Lee, C. 2004. Inferring 3d body pose from silhouettes using activity manifold learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Ellis, D. 2003. Dynamic time warp (DTW) in Matlab. www.ee.columbia.edu/~dpwe/resources/matlab/dtw/.Google ScholarGoogle Scholar
  13. Forsyth, D. A., Arikan, O., Ikemoto, L., O'Brien, J., and Ramanan, D. 2005. Computational studies of human motion: part 1, tracking and motion synthesis. Found. Trends Comput. Graph. Vis. 1, 2-3, 77--254. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Gleicher, M. 1998. Retargetting motion to new characters. In Proceedings of the ACM SIGGRAPH '98. 33--42. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Grochow, K., Martin, S. L., Hertzmann, A., and Popovic, Z. 2004. Implicit surface joint limits to constrain video-based motion capture. ACM Trans. Graph. 23, 3, 522--531.Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Hartley, R. and Zisserman, A. 2003. Multiple View Geometry, 2 ed. Cambridge University Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Herda, L., Urtasun, R., and Fua, P. 2004. Implicit surface joint limits to constrain video-based motion capture. In Proceedings of the European Conference on Computer Vision. 405--418.Google ScholarGoogle Scholar
  18. Hornung, A., Dekkers, E., and Kobbelt, L. 2007. Character animation from 2d pictures and 3d motion data. ACM Trans. Graph. 26, 1, 1--9. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Ikemoto, L., Arikan, O., and Forsyth, D. 2009. Generalizing motion edits with gaussian processes. ACM Trans. Graph. 28, 1, 1--12. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Jain, E., Sheikh, Y., and Hodgins, J. K. 2009. Leveraging the talent of hand animators to create three-dimensional animation. In Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Jain, E., Sheikh, Y., Mahler, M., and Hodgins, J. 2010. Augmenting hand animation with three-dimensional secondary motion. In Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Johnston, O. and Thomas, F. 1995. The Illusion of Life: Disney Animation. Disney Editions.Google ScholarGoogle Scholar
  23. Johnston, S. F. 2002. Lumo: Illumination for cel animation. In Proceedings of the (NPAR'02) Symposium on Non-Photorealistic Animation and Rendering. 45--52. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Lasseter, J. 1994. Tricks to animating characters with a computer. In ACM SIGGRAPH Course Notes. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Lee, H. J. and Chen, Z. 1985. Determination of 3d human body postures from a single view. Comput. Vis. Graph. Image Process. 30, 148--168.Google ScholarGoogle ScholarCross RefCross Ref
  26. Lee, J., Chai, J., Reitsma, P. S. A., Hodgins, J. K., and Pollard, N. S. 2002. Interactive control of avatars animated with human motion data. ACM Trans. Graph. 21, 3, 491--500. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Li, Y., Gleicher, M., Xu, Y.-Q., and Shum, H.-Y. 2003. Stylizing motion with drawings. In Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation, 309--319. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Moeslund, T. B. and Granum, E. 2006. A survey of computer vision-based human motion capture. Comput. Vis. Image Understand. 81, 3, 231--268. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Petrović, L., Fujito, B., Williams, L., and Finkelstein, A. 2000. Shadows for cel animation. In Proceedings of the ACM SIGGRAPH'00. 511--516. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Rademacher, P. 1999. View-dependent geometry. In Proceedings of the ACM SIGGRAPH'99 Conference. 439--446. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Ramanan, D., Forsyth, D., and Zisserman, A. 2004. Tracking people by learning their appearance. IEEE Trans. Pattern Anal. Mach. Intell. 29, 1, 65--81. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Rivers, A., Igarashi, T., and Durand, F. 2010. 2.5d cartoon models. ACM Trans. Graph. 29, 4. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Robertson, B. 1998. Mixed media. Comput. Graph. World, 32--35.Google ScholarGoogle Scholar
  34. Rosenhahn, B., Brox, T., Cremers, D., and Seidel, H.-P. 2007a. Online smoothing for markerless motion capture. In Proceedings of the DAGM Symposium on Pattern Recognition 4713, 163--172. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Rosenhahn, B., Brox, T., Cremers, D., and Seidel, H.-P. 2008. Staying well grounded in markerless motion capture. In Proceedings of the DAGM Symposium on Pattern Recognition 5096, 385--395. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Rosenhahn, B., Brox, T., and Seidel, H.-P. 2007b. Scaled motion dynamics for markerless motion capture. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Google ScholarGoogle Scholar
  37. Safonova, A., Hodgins, J. K., and Pollard, N. S. 2004. Synthesizing physically realistic human motion in low-dimensional, behavior-specific spaces. ACM Trans. Graph. 23, 3. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Sakoe, H. and Chiba, S. 1990. Dynamic programming algorithm optimization for spoken word recognition. Read. Speech Recogn. 159--165. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Sidenbladh, H., Black, M., and Sigal, L. 2002. Implicit probabilistic models of human motion for synthesis and tracking. In Proceedings of the European Conference on Computer Vision. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Sidenbladh, H., Black, M. J., and Fleet, D. J. 2000. Stochastic tracking of 3d human figures using 2d image motion. In Proceedings of the European Conference on Computer Vision, 702--718. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Sminchisescu, C., Kanaujia, A., and Metaxas, D. 2005. Discriminative density propagation for 3d human motion estimation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Sminchisescu, C. and Triggs, B. 2003. Estimating articulated human motion with covariance scaled sampling. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Google ScholarGoogle Scholar
  43. Stam, J. 2009. Nucleus: Towards a unified dynamics solver for computer graphics. In Proceedings of the IEEE International Conference on Computer-Aided Design and Computer Graphics, 1--11.Google ScholarGoogle ScholarCross RefCross Ref
  44. Sýkora, D., Sedláček, D., Jinchao, S., Dingliana, J., and Collins, S. 2010. Adding depth to cartoons using sparse depth (in)equalities. Comput. Graph. Forum 29, 2, 615--623.Google ScholarGoogle ScholarCross RefCross Ref
  45. Taylor, C. J. 2000. Reconstruction of articulated objects from point correspondences in a single uncalibrated image. Comput. Vis. Image Understand. 80, 349--363. Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. Urtasun, R., Fleet, D. J., and Fua, P. 2006. Temporal motion models for monocular and multiview 3d human body tracking. Comput. Vis. Image Understand. 104, 2, 157--177. Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. Wei, X. and Chai, J. 2010. Videomocap: Modeling physically realistic human motion from monocular video sequences. ACM Trans. Graph. 29, 4, 1--10. Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. Wood, D. N., Finkelstein, A., Hughes, J. F., Thayer, C. E., and Salesin, D. H. 1997. Multiperspective panoramas for cel animation. In Proceedings of the ACM SIGGRAPH'97 Conference, 243--250. Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. Wu, Y., Hua, G., and Yu, T. 2003. Tracking articulated body by dynamic markov network. In Proceedings of the IEEE International Conference on Computer Vision. Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. Zhao, L. and Safonova, A. 2009. Achieving good connectivity in motion graphs. Graph. Models 71, 4, 139--152. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Three-dimensional proxies for hand-drawn characters

        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 1
          January 2012
          149 pages
          ISSN:0730-0301
          EISSN:1557-7368
          DOI:10.1145/2077341
          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: 2 February 2012
          • Accepted: 1 September 2011
          • Revised: 1 August 2011
          • Received: 1 May 2011
          Published in tog Volume 31, Issue 1

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article
          • Research
          • Refereed

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader