Abstract
This article presents a new framework for synthesizing motion of a virtual character in response to the actions performed by a user-controlled character in real time. In particular, the proposed method can handle scenes in which the characters are closely interacting with each other such as those in partner dancing and fighting. In such interactions, coordinating the virtual characters with the human player automatically is extremely difficult because the system has to predict the intention of the player character. In addition, the style variations from different users affect the accuracy in recognizing the movements of the player character when determining the responses of the virtual character. To solve these problems, our framework makes use of the spatial relationship-based representation of the body parts called interaction mesh, which has been proven effective for motion adaptation. The method is computationally efficient, enabling real-time character control for interactive applications. We demonstrate its effectiveness and versatility in synthesizing a wide variety of motions with close interactions.
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- Arikan, O., Forsyth, D. A., and O'Brien, J. F. 2003. Motion synthesis from annotations. ACM Trans. Graph. 22, 3, 402--408. Google Scholar
Digital Library
- Chan, J. C. P., Leung, H., Tang, J. K. T., and Komura, T. 2011. A virtual reality dance training system using motion capture technology. IEEE Trans. Learn. Technol. 4, 2, 187--195. Google Scholar
Digital Library
- Davis, T. A. 2004. Algorithm 832: UMFPACK V4.3—An unsymmetric-pattern multifrontal method. ACM Trans. Math. Softw. 30, 2, 196--199. Google Scholar
Digital Library
- Deng, L., Leung, H., Gu, N., and Yang, Y. 2011. Real-time mocap dance recognition for an interactive dancing game. Comput. Animation Virtual Worlds 22, 2--3, 229--237. Google Scholar
Digital Library
- Gleicher, M. 1998. Retargetting motion to new characters. In Proceedings of the 25th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH'98). ACM Press, New York, 33--42. Google Scholar
Digital Library
- Goto, K. and Van De Geijn, R. 2008. High-performance implementation of the level-3 blas. ACM Trans. Math. Softw. 35, 1, 1--14. Google Scholar
Digital Library
- Grochow, K., Martin, S. L., Hertzmann, A., and Popovic, Z. 2004. Style-based inverse kinematics. ACM Trans. Graph. 23, 3, 522--531. Google Scholar
Digital Library
- Gross, R. and Shi, J. 2001. The cmu motion of body (mobo). Tech. rep. CMU-RI-TR-01-18. Database Robotics Institute, Carnegie Mellon University, Pittsburgh, PA. http://mocap.cs.cmu.edu.Google Scholar
- Ho, E. S. L. and Komura, T. 2009a. Character motion synthesis by topology coordinates. Comput. Graph. Forum 28, 2, 299--308.Google Scholar
Cross Ref
- Ho, E. S. L. and Komura, T. 2009b. Indexing and retrieving motions of characters in close contact. IEEE Trans. Vis. Comput. Graph. 15, 3, 481--492. Google Scholar
Digital Library
- Ho, E. S. L., Komura, T., and Tai, C.-L. 2010. Spatial relationship preserving character motion adaptation. ACM Trans. Graph. 29, 4, 1--8. Google Scholar
Digital Library
- Ho, E. S. L. and Komura, T. 2011. A finite state machine based on topology coordinates for wrestling games. Comput. Animat. Virtual Worlds 22, 5, 435--443. Google Scholar
Digital Library
- Hsu, E., Gentry, S., and Popovic, J. 2004. Example-based control of human motion. In Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA'04). Eurographics Association, 69--77. Google Scholar
Digital Library
- Ikemoto, L., Arikan, O., and Forsyth, D. 2007. Quick transitions with cached multi-way blends. In Proceedings of the Symposium on Interactive 3D Graphics and Games (I3D'07). ACM Press, New York, 145--151. Google Scholar
Digital Library
- Inamura, T., Nakamura, Y., Toshima, I., and Tanie, H. 2004. Embodied symbol emergence based on mimesis theory. Int. J. Robotics Res. 23, 4--5, 363--377.Google Scholar
Cross Ref
- Kovar, L. and Gleicher, M. 2004. Automated extraction and parameterization of motions in large data sets. ACM Trans. Graph. 23, 3, 559--568. Google Scholar
Digital Library
- Kovar, L., Gleicher, M., and Pighin, F. 2002. Motion graphs. ACM Trans. Graph. 21, 3, 473--482. Google Scholar
Digital Library
- Krüger, B., Tautges, J., Weber, A., and Zinke, A. 2010. Fast local and global similarity searches in large motion capture databases. In Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA'10). Eurographics Association, 1--10. Google Scholar
Digital Library
- Lee, D., Ott, C., and Nakamura, Y. 2009. Mimetic communication with impedance control for physical human-robot interaction. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'09). 1535--1542. Google Scholar
Digital Library
- 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 Scholar
Digital Library
- Lee, J. and Lee, K. H. 2004. Precomputing avatar behavior from human motion data. In Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA'04). Eurographics Association, 79--87. Google Scholar
Digital Library
- Liu, C. K., Hertzmann, A. and Popovic, Z. 2006. Composition of complex optimal multi-character motions. In Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA'06). Eurographics Association, 215--222. Google Scholar
Digital Library
- Magnenat-Thalmann, N., Protopsaltou, D., and Kavakli, E. 2008. Learning how to dance using a web 3d platform. In Proceedings of the 6th International Conference on Advances in Web Based Learning (ICWL'07). H. Leung, F. Li, R. Lau, and Q. Li, Eds., Lecture Notes in Computer Science, vol. 4823, Springer, 1--12. Google Scholar
Digital Library
- Mount, D. M. and Arya, S. 2006. ANN: A library for approximate nearest neighbor searching. Programming manual. College Park, Maryland. http://www.cs.umd.edu/∼mount/ANN/.Google Scholar
- Nakamura and Yamane Laboratory. 2005. Animatronic humanoid robot project in prototype robot exhibition, Aichi EXPO. http://www.ynl.t.u-tokyo.ac.jp/research/expo2005/expo2005-e.html.Google Scholar
- Nakayama, D., Kosuge, K., and Hirata, Y. 2009. Human-adaptive step estimation method for a dance partner robot. In Proceedings of the IEEE International Conference on Automation and Logistics (ICAL'09). 191--196.Google Scholar
- Sakai, Y., Takeda, T., Hirata, Y., and Kosuge, K. 2007. Collision avoidance based on estimated step of other dance couples for male-type dance partner robot. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'07). 3264--3269.Google Scholar
- Shum, H. P. H., Komura, T., and Yamazaki, S. 2007. Simulating competitive interactions using singly captured motions. In Proceedings of the ACM Symposium on Virtual Reality Software Technology. 65--72. Google Scholar
Digital Library
- Shum, H. P. H., Komura, T., and Yamazaki, S. 2008. Simulating interactions of avatars in high dimensional state space. In Proceedings of the Symposium on Interactive 3D Graphics and Games (I3D'08). ACM Press, New York, 131--138. Google Scholar
Digital Library
- Si, H. and Grtner, K. 2005. Meshing piecewise linear complexes by constrained delaunay tetrahedralizations. In Proceedings of the 14th International Meshing Roundtable, B. W. Hanks, Ed. Springer, 147--163.Google Scholar
- Sugihara, T. and Nakamura, Y. 2005. A fast online gait planning with boundary condition relaxation for humanoid robots. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'05). 305--310.Google Scholar
- Sugihara, T., Yamamoto, K., and Nakamura, Y. 2005. Architectural design of miniature anthropomorphic robots towards high mobility. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'05). 2869--2874.Google Scholar
- Takano, W., Imagawa, H., and Nakamura, Y. 2011. Prediction of human behaviors in the future through symbolic inference. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'11). 1970--1975.Google Scholar
- Takeda, T., Hirata, Y., and Kosuge, K. 2007a. Dance partner robot cooperative motion generation with adjustable length of dance step stride based on physical interaction. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'07). 3258--3263.Google Scholar
- Takeda, T., Hirata, Y., and Kosuge, K. 2007b. Dance step estimation method based on hmm for dance partner robot. IEEE Trans. Indust. Electron. 54, 2, 699--706.Google Scholar
Cross Ref
- Takeda, T., Kosuge, K., and Hirata, Y. 2005. HMM-based dance step estimation for dance partner robot -ms dancer. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'05). 3245--3250.Google Scholar
- Tang, J. K. T., Chan, J. C. P., and Leung, H. 2011. Interactive dancing game with real-time recognition of continuous dance moves from 3D human motion capture. In Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication (ICUIMC'11). ACM Press, New York. Google Scholar
Digital Library
- Treuille, A., Lee, Y., and Popovic, Z. 2007. Near-optimal character animation with continuous control. In ACM SIGGRAPH Papers. ACM Press, New York. Google Scholar
Digital Library
- Tsuruta, S., Kawauchi, Y., Choi, W., and Hachimura, K. 2007. Real-time recognition of body motion for virtual dance collaboration system. In Proceedings of the International Conference on Artificial Reality and Telexistence. IEEE Computer Society, 23--30. Google Scholar
Digital Library
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Interactive partner control in close interactions for real-time applications
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