skip to main content
research-article

Capture and modeling of non-linear heterogeneous soft tissue

Published:27 July 2009Publication History
Skip Abstract Section

Abstract

This paper introduces a data-driven representation and modeling technique for simulating non-linear heterogeneous soft tissue. It simplifies the construction of convincing deformable models by avoiding complex selection and tuning of physical material parameters, yet retaining the richness of non-linear heterogeneous behavior. We acquire a set of example deformations of a real object, and represent each of them as a spatially varying stress-strain relationship in a finite-element model. We then model the material by non-linear interpolation of these stress-strain relationships in strain-space. Our method relies on a simple-to-build capture system and an efficient run-time simulation algorithm based on incremental loading, making it suitable for interactive computer graphics applications. We present the results of our approach for several non-linear materials and biological soft tissue, with accurate agreement of our model to the measured data.

Skip Supplemental Material Section

Supplemental Material

tps064_09.mp4

mp4

48.3 MB

References

  1. Allen, B., Curless, B., and Popović, Z. 2002. Articulated body deformation from range scan data. ACM Trans. Graph. 21, 3, 612--619. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Bathe, K.-J. 1995. Finite Element Procedures. Prentice-Hall.Google ScholarGoogle Scholar
  3. Becker, M., and Teschner, M. 2007. Robust and efficient estimation of elasticity parameters using the linear finite element method. In SimVis, 15--28.Google ScholarGoogle Scholar
  4. Bergeron, P., and Lachapelle, P., 1985. Controlling facial expression and body movements in the computer generated short "Tony de Peltrie". Siggraph Course Notes.Google ScholarGoogle Scholar
  5. Bickel, B., Lang, M., Botsch, M., Otaduy, M. A., and Gross, M. 2008. Pose-space animation and transfer of facial details. In Proc. of the ACM SIGGRAPH / Eurographics Symposium on Computer Animation, 57--66. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Blanz, V., Basso, C., Poggio, T., and Vetter, T. 2003. Re-animating faces in images and video. Computer Graphics Forum 22, 3 (Sept.), 641--650.Google ScholarGoogle ScholarCross RefCross Ref
  7. Botsch, M., and Sorkine, O. 2008. On linear variational surface deformation methods. IEEE Transactions on Visualization and Computer Graphics (TVCG) 14, 1, 213--230. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Buehler, C., Bosse, M., McMillan, L., Gortler, S., and Cohen, M. 2001. Unstructured lumigraph rendering. In Proc. of ACM SIGGRAPH, ACM, 425--432. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Burion, S., Conti, F., Petrovskaya, A., Baur, C., and Khatib, O. 2008. Identifying physical properties of deformable objects by using particle filters. In Proc. of the International Conference on Robotics and Automation, 1112--1117.Google ScholarGoogle Scholar
  10. Capell, S., Burkhart, M., Curless, B., Duchamp, T., and Popović, Z. 2005. Physically based rigging for deformable characters. In ACM SIGGRAPH/Eurographics Symposium on Computer Animation, 301--310. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Carr, J. C., Beatson, R. K., Cherrie, J. B., Mitchell, T. J., Fright, W. R., McCallum, B. C., and Evans, T. R. 2001. Reconstruction and representation of 3D objects with radial basis functions. In Proc. of ACM SIGGRAPH, 67--76. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. DiLorenzo, P., Zordan, V., and Sanders, B. 2008. Laughing Out Loud: Control for modeling anatomically inspired laughter using audio. ACM Trans. Graph. (Proc. of ACM SIGGRAPH Asia) 27, 5. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Galoppo, N., Otaduy, M. A., Moss, W., Sewall, J., Curtis, S., and Lin, M. C. 2009. Controlling deformable material with dynamic morph targets. In ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Hart, E. W. 1967. Theory of the tensile test. Acta Metallurgica 15, 351--355.Google ScholarGoogle ScholarCross RefCross Ref
  15. Hughes, T. J. R. 2000. The Finite Element Method. Linear Static and Dynamic Finite Element Analysis. Dover Publications.Google ScholarGoogle Scholar
  16. James, D. L., and Pai, D. K. 1999. ArtDefo: Accurate real time deformable objects. In Proc. of ACM SIGGRAPH, ACM Press/Addison-Wesley Publishing Co., 65--72. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Kajberg, J., and Lindkvist, G. 2004. Characterisation of materials subjected to large strains by inverse modelling based on in-plane displacement fields. International Journal of Solids and Structures 41, 13, 3439--3459.Google ScholarGoogle ScholarCross RefCross Ref
  18. Kauer, M., Vuskovic, V., Dual, J., Szekely, G., and Bajka, M. 2002. Inverse finite element characterization of soft tissues. Medical Image Analysis 6, 3, 257--287.Google ScholarGoogle ScholarCross RefCross Ref
  19. Kaufmann, P., Martin, S., Botsch, M., and Gross, M. 2008. Flexible simulation of deformable models using discontinuous galerkin fem. Proceedings of the ACM SIGGRAPH / Eurographics Symposium on Computer Animation, 105--115. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Kim, T.-Y., and Vendrovsky, E. 2008. Drivenshape - a data-driven approach to shape deformation. In Proc. of the ACM SIGGRAPH / Eurographics Symposium on Computer Animation. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Koch, R. M., Gross, M. H., Carls, F. R., von Büren, D. F., Fankhauser, G., and Parish, Y. 1996. Simulating facial surgery using finite element methods. In Proc. of ACM SIGGRAPH, 421--428. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Kry, P. G., and Pai, D. K. 2006. Interaction capture and synthesis. ACM Trans. Graph. (Proc. of ACM SIGGRAPH) 25, 3, 872--880. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Lang, J., Pai, D. K., and Woodham, R. J. 2002. Acquisition of elastic models for interactive simulation. International Journal of Robotics Research 21, 8, 713--733.Google ScholarGoogle ScholarCross RefCross Ref
  24. Lee, S.-H., and Terzopoulos, D. 2006. Heads up!: Biomechanical modeling and neuromuscular control of the neck. ACM Trans. Graph. (Proc. of ACM SIGGRAPH) 25, 3. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Levenberg, K. 1944. A method for the solution of certain non-linear problems in least squares. The Quarterly of Applied Mathematics, 2, 164--168.Google ScholarGoogle ScholarCross RefCross Ref
  26. Lewis, J. P., Cordner, M., and Fong, N. 2000. Pose space deformation: A unified approach to shape interpolation and skeleton-driven deformation. In Proc. of ACM SIGGRAPH, 165--172. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Ma, W.-C., Jones, A., Chiang, J.-Y., Hawkins, T., Frederiksen, S., Peers, P., Vukovic, M., Ouhyoung, M., and Debevec, P. 2008. Facial performance synthesis using deformation-driven polynomial displacement maps. ACM Trans. Graph. (Proc. of ACM SIGGRAPH Asia) 27, 5. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Magnenat-Thalmann, N., Kalra, P., Lévêque, J. L., Bazin, R., Batisse, D., and Queleux, B. 2002. A computational skin model: fold and wrinkle formation. IEEE Trans. on Information Technology in Biomedicine 6, 4, 317--323. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Matusik, W., Pfister, H., Brand, M., and McMillan, L. 2003. A data-driven reflectance model. ACM Transactions on Graphics (Proc. of ACM SIGGRAPH) 22, 3, 759--770. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Müller, M., and Gross, M. 2004. Interactive virtual materials. In GI '04: Proceedings of Graphics Interface 2004, Canadian Human-Computer Communications Society, School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada, 239--246. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Nava, A., Mazza, E., Kleinermann, F., Avis, N. J., and McClure, J. 2003. Determination of the mechanical properties of soft human tissues through aspiration experiments. In Proc. of MICCAI, 222--229.Google ScholarGoogle ScholarCross RefCross Ref
  32. Nealen, A., Mller, M., Keiser, R., Boxerman, E., and Carlson, M. 2006. Physically based deformable models in computer graphics. Computer Graphics Forum 25, 4 (Dec.), 809--836.Google ScholarGoogle ScholarCross RefCross Ref
  33. Ogden, R. W. 1997. Non-Linear Elastic Deformations. Courier Dover Publications.Google ScholarGoogle Scholar
  34. Ottensmeyer, M. P., and Salisbury Jr., J. K. 2004. In-vivo data acquisition instrument for solid organ mechanical property measurement. In Proc. of MICCAI, 975--982. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Pai, D. K., van den Doel, K., James, D. L., Lang, J., Lloyd, J. E., Richmond, J. L., and Yau, S. H. 2001. Scanning physical interaction behavior of 3d objects. In Proceedings of ACM SIGGRAPH, 87--96. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Park, S. I., and Hodgins, J. K. 2006. Capturing and animating skin deformation in human motion. ACM Transactions on Graphics (Proc. of ACM SIGGRAPH) 25, 3. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Park, S. I., and Hodgins, J. K. 2008. Data-driven modeling of skin and muscle deformation. ACM Transactions on Graphics (Proc. of ACM SIGGRAPH) 27, 3. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Schnur, D. S., and Zabaras, N. 1992. An inverse method for determining elastic material properties and a material interface. International Journal for Numerical Methods in Engineering 33, 10, 2039--2057.Google ScholarGoogle ScholarCross RefCross Ref
  39. Schoner, J. L., Lang, J., and Seidel, H.-P. 2004. Measurement-based interactive simulation of viscoelastic solids. Computer Graphics Forum (Proc. Eurographics) 23, 3, 547--556.Google ScholarGoogle ScholarCross RefCross Ref
  40. Sifakis, E., Neverov, I., and Fedkiw, R. 2005. Automatic determination of facial muscle activations from sparse motion capture marker data. ACM Transactions on Graphics (Proc. of ACM SIGGRAPH) 24, 3, 417--425. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Sloan, P.-P. J., Rose, III, C. F., and Cohen, M. F. 2001. Shape by example. In I3D '01: Proceedings of the 2001 symposium on Interactive 3D graphics, ACM, New York, NY, USA, 135--143. Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Sueda, S., Kaufman, A., and Pai, D. K. 2008. Musculotendon simulation for hand animation. ACM Trans. Graph. (Proc. SIGGRAPH) 27, 3. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Sumner, R. W., Zwicker, M., Gotsman, C., and Popović, J. 2005. Mesh-based inverse kinematics. In ACM Trans. on Graphics (Proc. of ACM SIGGRAPH), vol. 24, 488--495. Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. Teran, J., Sifakis, E., Blemker, S., Ng Thow Hing, V., Lau, C., and Fedkiw, R. 2005. Creating and simulating skeletal muscle from the visible human data set. IEEE TVCG 11, 317--328. Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. Terzopoulos, D., Platt, J., Barr, A., and Fleischer, K. 1987. Elastically deformable models. In Proc. of ACM SIGGRAPH 87, 205--214. Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. Terzopoulus, D., and Waters, K. 1993. Analysis and synthesis of facial image sequences using physical and anatomical models. IEEE Trans. PAMI 14 (June), 569--579. Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. Toledo, S., Chen, D., and Rotkin, V. 2003. Taucs: A library for sparse linear solvers.Google ScholarGoogle Scholar
  48. Zhang, H. 2004. Discrete combinatorial laplacian operators for digital geometry processing. In Proc. of SIAM Conference on Geometric Design and Computing, Nashboro Press, 575--592.Google ScholarGoogle Scholar
  49. Zordan, V., Celly, B., Chiu, B., and Dilorenzo, P. C. 2004. Breathe easy: Model and control of human respiration for computer animation. In Proc. of the ACM SIGGRAPH / Eurographics Symposium on Computer Animation, 29--38. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Capture and modeling of non-linear heterogeneous soft tissue

        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 28, Issue 3
          August 2009
          750 pages
          ISSN:0730-0301
          EISSN:1557-7368
          DOI:10.1145/1531326
          Issue’s Table of Contents

          Copyright © 2009 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: 27 July 2009
          Published in tog Volume 28, Issue 3

          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