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Tytuł artykułu

On the Representation of Human Motions and Distance-based Retargeting

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Konferencja
Federated Conference on Computer Science and Information Systems (16 ; 02-05.09.2021 ; online)
Języki publikacji
EN
Abstrakty
EN
Distance-based motion adaptation leads to the formulation of a dynamical Distance Geometry Problem (dynDGP) where the involved distances simultaneously represent the morphology of the animated character, as well as a possible motion. The explicit use of inter-joint distances allows us to easily verify the presence of joint contacts, which one generally wishes to preserve when adapting a given motion to characters having a different morphology. In this work, we focus our attention on suitable representations of human-like animated characters, and study the advantages (and disadvantages) in using some of them. In the initial works on distance-based motion adaptation, a 3ndimensional vector was employed for representing the positions of the n joints of the character at a given frame. Here, we investigate the use of another, very popular in computer graphics, representation that basically replaces every joint position in the three-dimensional space with a set of three sorted Euler angles. We show that the latter can in fact be useful for avoiding some of the artifacts that were observed in previous computational experiments, but we argue that this Euler-angle representation, from a motion adaptation point of view, does not seem to be the optimal one. By paying particular attention to the degrees of freedom of the studied representations, it turns out that a novel character representation, inspired by representations used in structural biology for molecules, may allow us to reduce the character degrees of freedom to their minimal value. As a result, statistical analysis on human motion databases, where the motions are given with this new representation, can potentially provide important insights on human motions. This study is an initial step towards the identification of a full set of constraints capable of ensuring that unnatural postures for humans cannot be created while tackling motion adaptation problems.
Rocznik
Tom
Strony
181--189
Opis fizyczny
Bibliogr. 23 poz., wz., rys., wykr.
Twórcy
  • IRISA, University of Rennes 1, Rennes, France
  • IRISA, University of Rennes 1, Rennes, France
Bibliografia
  • 1. A. Alfakih, Universal Rigidity of Bar Frameworks in General Position: a Euclidean Distance Matrix Approach. In: [18], Springer, 3–22, 2013.
  • 2. H. Berman, J. Westbrook, Z. Feng, G. Gilliland, T. Bhat, H. Weissig, I. Shindyalov, P. Bourne, The Protein Data Bank, Nucleic Acids Research 28, 235–242, 2000.
  • 3. A. Bernardin, L. Hoyet, A. Mucherino, D.S. Gonçalves, F. Multon, Normalized Euclidean Distance Matrices for Human Motion Retargeting, ACM Conference Proceedings, Motion in Games 2017 (MIG17), Barcelona, Spain, November 2017.
  • 4. J. Diebel, Representing Attitude: Euler angles, Unit Quaternions, and Rotation Vectors, Matrix 58(15–16), 1–35, 2006.
  • 5. R. Featherstone, Rigid Body Dynamics Algorithms, Springer, 279 pages, 2008.
  • 6. M. Gleicher, Retargetting Motion to New Characters. ACM Proceedings of the 25th annual conference on Computer Graphics and Interactive Techniques, 33–42, 1998.
  • 7. S. Guo, R. Southern, J. Chang, D. Greer, J. J. Zhang, Adaptive Motion Synthesis for Virtual Characters: a Survey, The Visual Computer 31(5), 497–512. 2015.
  • 8. E. S. L Ho, T. Komura, C-L. Tai, Spatial Relationship Preserving Character Motion Adaptation, Proceedings of the 37 th International Conference and Exhibition on Computer Graphics and Interactive Techniques, ACM Transactions on Graphics 29(4), 8 pages, 2010.
  • 9. G. Laman, On Graphs and Rigidity of Plane Skeletal Structures, Journal of Engineering Mathematics 4(4), 331–340, 1970.
  • 10. L. Liberti, C. Lavor, N. Maculan, A. Mucherino, Euclidean Distance Geometry and Applications, SIAM Review 56(1), 3–69, 2014.
  • 11. T. E. Malliavin, A. Mucherino, M. Nilges, Distance Geometry in Structural Biology: New Perspectives. In: [18], Springer, 329–350, 2013.
  • 12. W. Maurel, D. Thalmann, Human Shoulder Modeling Including Scapulo-Thoracic Constraint and Joint Sinus Cones, Computers & Graphics 24, 203–218, 2000.
  • 13. M. Meredith, S. Maddock, Motion Capture File Formats Explained, Technical Report 211, Department of Computer Science, University of Sheffield, 36 pages, 2001.
  • 14. J.-S. Monzani, P. Baerlocher, R. Boulic, D. Thalmann, Using an Intermediate Skeleton and Inverse Kinematics for Motion Retargeting, Computer Graphics Forum 19(3), 11–19, 2000.
  • 15. A. Mucherino, Introducing the Interaction Distance in the context of Distance Geometry for Human Motions, Chebyshevskii sbornik 20(2), 263–273, 2019.
  • 16. A. Mucherino, D. S. Gonçalves, An Approach to Dynamical Distance Geometry, Lecture Notes in Computer Science 10589, F. Nielsen, F. Barbaresco (Eds.), Proceedings of Geometric Science of Information (GSI17), Paris, France, 821–829, 2017.
  • 17. A. Mucherino, D.S. Gonçalves, A. Bernardin, L. Hoyet, F. Multon, A Distance-Based Approach for Human Posture Simulations, IEEE Conference Proceedings, Federated Conference on Computer Science and Information Systems (FedCSIS17), Workshop on Computational Optimization (WCO17), Prague, Czech Republic, 441–444, 2017.
  • 18. A. Mucherino, C. Lavor, L. Liberti, N. Maculan (Eds.), Distance Geometry: Theory, Methods and Applications, 410 pages, Springer, 2013.
  • 19. A. Mucherino, J. Omer, L. Hoyet, P. Robuffo Giordano, F. Multon, An Application-based Characterization of Dynamical Distance Geometry Problems, Optimization Letters 14(2), 493–507, 2020.
  • 20. G. N. Ramachandran, C. Ramakrishnan, V. Sasisekharan, Stereochemistry of Polypeptide Chain Configurations, Journal of Molecular Biology 7, 95–104, 1963.
  • 21. J. Saxe, Embeddability of Weighted Graphs in k-Space is Strongly NP-hard, Proceedings of 17th Allerton Conference in Communications, Control and Computing, 480–489, 1979.
  • 22. G.G. Slabaugh, Computing Euler Angles from a Rotation Matrix, Technical Report, City University London, 8 pages, 1999.
  • 23. P. Tabaghi, I. Dokmanić, M. Vetterli, Kinetic Euclidean Distance Matrices, IEEE Transactions on Signal Processing 68, 452–465, 2020.
Uwagi
1. This work is partially supported by the international project MULTIBIOSTRUCT funded by the ANR French funding agency (ANR-19-CE45-0019).
2. Track 1: Artificial Intelligence in Applications
3. Session: 14th International Workshop on Computational Optimization
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-afcd3cd3-e546-4536-824c-ed54beede129
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