“Style-based inverse kinematics” by Grochow, Hertzmann and Popovic

This paper presents an inverse kinematics system based on a learned model of human poses. Given a set of constraints, our system can produce the most likely pose satisfying those constraints, in real-time. Training the model on different input data leads to different styles of IK. The model is represented as a probability distribution over the space of all possible poses. This means that our IK system can generate any pose, but prefers poses that are most similar to the space of poses in the training data. We represent the probability with a novel model called a Scaled Gaussian Process Latent Variable Model. The parameters of the model are all learned automatically; no manual tuning is required for the learning component of the system. We additionally describe a novel procedure for interpolating between styles.Our style-based IK can replace conventional IK, wherever it is used in computer animation and computer vision. We demonstrate our system in the context of a number of applications: interactive character posing, trajectory keyframing, real-time motion capture with missing markers, and posing from a 2D image.

1. ARIKAN, O., AND FORSYTH, D. A. 2002. Synthesizing Constrained Motions from Examples. ACM Transactions on Graphics 21, 3 (July), 483–490. (Proc. of ACM SIGGRAPH 2002). Google ScholarDigital Library
2. ARIKAN, O., FORSYTH, D. A., AND O’BRIEN, J. F. 2003. Motion Synthesis From Annotations. ACM Transactions on Graphics 22, 3 (July), 402–408. (Proc. SIGGRAPH 2003). Google ScholarDigital Library
3. BISHOP, C. M. 1995. Neural Networks for Pattern Recognition. Oxford University Press. Google ScholarDigital Library
4. BODENHEIMER, B., ROSE, C., ROSENTHAL, S., AND PELLA, J. 1997. The process of motion capture — dealing with the data. In Computer Animation and Simulation ’97, Springer-Verlag Wien New York, D. Thalmann and M. van de Panne, Eds., Eurographics, 3–18.Google ScholarCross Ref
5. BRAND, M., AND HERTZMANN, A. 2000. Style machines. Proceedings of SIGGRAPH 2000 (July), 183–192. Google ScholarDigital Library
6. BRAND, M. 1999. Shadow Puppetry. In Proc. ICCV, vol. 2, 1237–1244. Google ScholarDigital Library
7. BRUDERLIN, A., AND WILLIAMS, L. 1995. Motion signal processing. Proceedings of SIGGRAPH 95 (Aug.), 97–104. Google ScholarDigital Library
8. ELKOURA, G., AND SINGH, K. 2003. Handrix: Animating the Human Hand. Proc. SCA, 110–119. Google ScholarDigital Library
9. GIRARD, M., AND MACIEJEWSKI, A. A. 1985. Computational Modeling for the Computer Animation of Legged Figures. In Computer Graphics (Proc. of SIGGRAPH 85), vol. 19, 263–270. Google ScholarDigital Library
10. GRASSIA, F. S. 2000. Believable Automatically Synthesized Motion by Knowledge-Enhanced Motion Transformation. PhD thesis, CMU Computer Science. Google ScholarDigital Library
11. GRAUMAN, K., SHAKHNAROVICH, G., AND DARRELL, T. 2003. Inferring 3D Structure with a Statistical Image-Based Shape Model. In Proc. ICCV, 641–648. Google ScholarDigital Library
12. GULLAPALLI, V., GELFAND, J. J., AND LANE, S. H. 1996. Synergy-based learning of hybrid position/force control for redundant manipulators. In Proceedings of IEEE Robotics and Automation Conference, 3526–3531.Google ScholarCross Ref
13. HOWE, N. R., LEVENTON, M. E., AND FREEMAN, W. T. 2000. Bayesian Reconstructions of 3D Human Motion from Single-Camera Video. In Proc. NIPS 12, 820–826.Google Scholar
14. KOVAR, L., AND GLEICHER, M. 2004. Automated Extraction and Parameterization of Motions in Large Data Sets. ACM Transactions on Graphics 23, 3 (Aug.). In these proceedings. Google ScholarDigital Library
15. KOVAR, L., GLEICHER, M., AND PIGHIN, F. 2002. Motion Graphs. ACM Transactions on Graphics 21, 3 (July), 473–482. (Proc. SIGGRAPH 2002). Google ScholarDigital Library
16. LAWRENCE, N., SEEGER, M., AND HERBRICH, R. 2003. Fast Sparse Gaussian Process Methods: The Informative Vector Machine. Proc. NIPS 15, 609–616.Google Scholar
17. LAWRENCE, N. D. 2004. Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data. Proc. NIPS 16.Google Scholar
18. 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 Transactions on Graphics 21, 3 (July), 491–500. (Proc. SIGGRAPH 2002). Google ScholarDigital Library
19. LI, Y., WANG, T., AND SHUM, H.-Y. 2002. Motion Texture: A Two-Level Statistical Model for Character Motion Synthesis. ACM Transactions on Graphics 21, 3 (July), 465–472. (Proc. SIGGRAPH 2002). Google ScholarDigital Library
20. MACKAY, D. J. C. 1998. Introduction to Gaussian processes. In Neural Networks and Machine Learning, C. M. Bishop, Ed., NATO ASI Series. Kluwer Academic Press, 133–166.Google Scholar
21. NEAL, R. M. 1996. Bayesian Learning for Neural Networks. Lecture Notes in Statistics No. 118. Springer-Verlag. Google ScholarDigital Library
22. NOCEDAL, J., AND WRIGHT, S. J. 1999. Numerical Optimization. Springer-Verlag.Google Scholar
23. O’HAGAN, A. 1978. Curve Fitting and Optimal Design for Prediction. J. of the Royal Statistical Society, ser. B 40, 1–42.Google Scholar
24. POPOVIĆ, Z., AND WITKIN, A. P. 1999. Physically Based Motion Transformation. Proceedings of SIGGRAPH 99 (Aug.), 11–20. Google ScholarDigital Library
25. PULLEN, K., AND BREGLER, C. 2002. Motion Capture Assisted Animation: Texturing and Synthesis. ACM Transactions on Graphics 21, 3 (July), 501–508. (Proc. of ACM SIGGRAPH 2002). Google ScholarDigital Library
26. RAMANAN, D., AND FORSYTH, D. A. 2004. Automatic annotation of everyday movements. In Proc. NIPS 16.Google Scholar
27. REDNER, R. A., AND WALKER, H. F. 1984. Mixture Densities, Maximum Likelihood and the EM Algorithm. SIAM Review 26, 2 (Apr.), 195–202.Google ScholarDigital Library
28. ROSALES, R., AND SCLAROFF, S. 2002. Learning Body Pose Via Specialized Maps. In Proc. NIPS 14, 1263–1270.Google Scholar
29. ROSE, C., COHEN, M. F., AND BODENHEIMER, B. 1998. Verbs and Adverbs: Multidimensional Motion Interpolation. IEEE Computer Graphics & Applications 18, 5, 32–40. Google ScholarDigital Library
30. ROSE III, C. F., SLOAN, P.-P. J., AND COHEN, M. F. 2001. Artist-Directed Inverse-Kinematics Using Radial Basis Function Interpolation. Computer Graphics Forum 20, 3, 239–250.Google ScholarCross Ref
31. SIDENBLADH, H., BLACK, M. J., AND SIGAL, L. 2002. Implicit probabilistic models of human motion for synthesis and tracking. In Proc. ECCV, LNCS 2353, vol. 1, 784–800. Google ScholarDigital Library
32. SNELSON, E., RASMUSSEN, C. E., AND GHAHRAMANI, Z. 2004. Warped Gaussian Processes. Proc. NIPS 16.Google Scholar
33. TAYLOR, C. J. 2000. Reconstruction of Articulated Objects from Point Correspondences in a Single Image. In Proc. CVPR, 677–684.Google ScholarCross Ref
34. WELMAN, C. 1993. Inverse Kinematics and Geometric Constraints for Articulated Figure Manipulation. PhD thesis, Simon Fraser University.Google Scholar
35. WILEY, D. J., AND HAHN, J. K. 1997. Interpolation Synthesis of Articulated Figure Motion. IEEE Computer Graphics & Applications 17, 6 (Nov.), 39–45. Google ScholarDigital Library
36. WILLIAMS, C. K. I., AND RASMUSSEN, C. E. 1996. Gaussian Processes for Regression. Proc. NIPS 8, 514–520.Google Scholar
37. WILSON, A. D., AND BOBICK, A. F. 1999. Parametric Hidden Markov Models for Gesture Recognition. IEEE Trans. PAMI 21, 9 (Sept.), 884–900. Google ScholarDigital Library
38. WITKIN, A., AND POPOVIĆ, Z. 1995. Motion Warping. Proceedings of SIGGRAPH 95 (Aug.), 105–108. Google ScholarDigital Library
39. YAMANE, K., AND NAKAMURA, Y. 2003. Natural motion animation through constraining and deconstraining at will. IEEE Transactions on Visualization and Computer Graphics 9, 3 (July), 352–360. Google ScholarDigital Library
40. ZHAO, L., AND BADLER, N. 1998. Gesticulation Behaviors for Virtual Humans. In Pacific Graphics ’98, 161–168. Google ScholarDigital Library