1. Niels Mulder & Floris de Vries
Motion Grammars for Character Animation
Kyunglyul Hyun, Kyungho Lee, and Jehee Lee
Niels Mulder & Floris de Vries

2. Overview
Goal: Creating a (context-free) grammar to describe and animate basketball.
Motion grammar
Multi-Level Markov Chain Monte Carlo
Tactic board

3. Related work

4. Constrained optimization
The continuous editing of motion is usually formulated as constrained optimization, which minimizes the deviation from the original motion data subject to user-specified constraints and requirements. This formulation has been effective for a wide range of problems, such as retargeting motion to new characters [Gle98], interactive manipulation [LS99], blending a family of similar motions [RCB98], statistical modeling [MCC09,MC12], and incorporating physics-based objectives and constraints [LHP05]. The idea has further been explored to deal with multiple interacting characters in the context of interactive manipulation [KLLT08,KHKL09, KSKL14]
Gleicher, M. (1998, July). Retargetting motion to new characters. In Proceedings of the 25th annual conference on Computer graphics and interactive techniques (pp ). ACM.
Kim, M., Hyun, K., Kim, J., & Lee, J. (2009). Synchronized multi-character motion editing. ACM transactions on graphics (TOG), 28(3), 79.

5. Data structures
The combinatorial planning of action sequences often requires an efficient data structure to store and search motion data. The most popular structure is a motion graph [LCR⇤02,KGP02], which is essentially a finite state machine encapsulating the connectivity among motion fragments. The concept of motion graphs has further been elaborated to cope with families of parameterized motions [SO06]. Good segmentation and clustering of motion fragments are key ingredients of building effective motion data structures [BSP⇤04, BCvdPP08]. Provided that such a structure is built, synthesizing novel motion sequences entails combinatorial searching through the connectivity among motion fragments. Temporal sequencing of motion fragments has been addressed by using state-space search [LCR⇤02, KGP02, SH07], dynamic programming [AFO03], min-max search [SKY12], and policy learning [MP07, TLP07]. State-space search methods are closely related to the path planning of three-dimensional characters in highly-constrained and dynamically-changing environments [CKHL11,LLKP11].
Lee, J., Chai, J., Reitsma, P. S., Hodgins, J. K., & Pollard, N. S. (2002, July). Interactive control of avatars animated with human motion data. In ACM Transactions on Graphics (ToG) (Vol. 21, No. 3, pp ). ACM.
Beaudoin, P., Coros, S., van de Panne, M., & Poulin, P. (2008, July). Motion-motif graphs. In Proceedings of the 2008 ACM SIGGRAPH/Eurographics Symposium on Computer Animation (pp ). Eurographics Association.

6. Continuous optimization and combinatorial planning integration
Lee, Y., Wampler, K., Bernstein, G., Popović, J., & Popović, Z. (2010, December). Motion fields for interactive character locomotion. In ACM Transactions on Graphics (TOG) (Vol. 29, No. 6, p. 138). ACM.

7. Motion patches
Kim, M., Hwang, Y., Hyun, K., & Lee, J. (2012, July). Tiling motion patches. In Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation (pp ). Eurographics Association.

8. Human action grammar
Park, J. P., Lee, K. H., & Lee, J. (2011, December). Finding syntactic structures from human motion data. In Computer Graphics Forum (Vol. 30, No. 8, pp ). Oxford, UK: Blackwell Publishing Ltd.

9. Grammars

10. Motion Grammar

11. Motion Grammar
Instantiation
Semantics
Plausability

12. Motion Grammar
Instantiation
Semantics
Plausability

13. Motion Grammar Instantiation Semantics Plausability
gparse is a binary boolean function, of which value is 1 if the string Xi has a parse tree and 0 otherwise
guser: the following equation evaluates how well X matches the description of an individual player.

14. Tactic Board

15. Probability distribution

16. Multi-Level Markov Chain Monte Carlo

17. Markov Chain Monte Carlo

18. Algorithm
Metropolis-Hastings

19. Random walks Motion clips Parse tree subtree Random clips
Mean probability of error of children

20. Parallel Tempering Conversion speed
Jump size <> Local extrema frequency

22. Critical Analysis

23. The Good Novel idea Good explanation of some the background Structure
Good balance of terminology and expertise
Pseudo-code, appendix

24. The Bad Slow Use cases unclear Language structure
Weird setup difference
Figure (log but no log?)
Self-praising without actually testing

25. Future Work Inferring motion grammars automatically
Practical implementation of combination of frameworks
Bringing it to real-time

26. ?