3 Hill Fig. 3 Hill s muscle model Fig. 1 Mainly the lower limb musculoskeletal model 2 Fig. 2 Link structure of the bone OpenSim 4)5) 2.2 Open

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1 CPG 1 1 CG CPG(Central Pattern Generator) Research on Generating Human Motion Using Musculoskeletal Model and CPG Akira Okazaki 1 and DongSheng Cai 1 Animation is one of the key elements of the CG, and there is strong demand that is the generation of human motion from many sectors. Recently, the purpose of many existing studies is reducing labor costs and realization of a natural motion. Based on these, we propose method of generating human motion using musculoskeletal model and CPG, and our method aims at autonomous and precise. In addition, we generate a walking motion. 1. (CG) CG 1 University of Tsukuba 1) (IK) 2) CPG FD IK 3) 1 c 2012 Information Processing Society of Japan

3 Hill Fig. 3 Hill s muscle model 2 1 2 1 Fig. 1 Mainly the lower limb musculoskeletal model 2 Fig. 2 Link structure of the bone OpenSim 4)5) 2.

2 3 Hill Fig. 3 Hill s muscle model Fig. 1 Mainly the lower limb musculoskeletal model 2 Fig. 2 Link structure of the bone OpenSim 4)5) 2.2 OpenSim (Ball Joint) 1 (Pin Joint) Hill Hill 6) Hill 3 (Contractile Element:CE) 2 c 2012 Information Processing Society of Japan

3 4 Fig. 4 f ce, l M f ce, l M curve 5 Fig. 5 f pee, l M f pee, l M curve 6 Fig. 6 f T, l T f T, l T curve 7 Fig. 7 M, v M M, v M curve (Parallel Elastic Element:PEE) (Series Elastic Element:SEE) 3 CE CE PEE SEE Hill α (Pennation Angle) fo M (Muscle Maximum) lo M (Muscle Optimal Length) ls T (Tendon Slack Length) v o (Contractile Max Velocity) OpenSim 7) Hill ( 1 ) (CE) (l M ) (lo M ) 4 ( l M = lm ) l M o CE ( f ce = f ce ) l M l M fo M o l M 1 f ce 1 ( 2 ) (PEE) (l M ) (lo M ) 5 ( l M = lm ) l M o PEE ( f pee = f pee ) l M = 1 l M f pee ( 3 ) f M o (SEE) PEE (l T ) (ls T ) 6 ( l T = lt ) l T s SEE ( f T = f T ) l T = 1 l T f T fo M ( 4 ) CE PEE (f M = f ce + f pee ) (v M ) ( f M = f ce + f pee ) ( v M = v M vo ) CE 8) a 0.0 a c 2012 Information Processing Society of Japan

4 OpenSim 3. CPG 8 Fig. 8 M, l M (a = 1.0) M, l M curve(a = 1.0) 10 Fig Fig. 9 M, v M (a = 0.2) M, v M curve(a = 0.2) M, l M (a = 0.2) M, l M curve(a = 0.2) 1.0 ( f M ) CE PEE SEE 6 f M, lm f M, vm CPG(Central Pattern Generator) CPG 9) CPG CPG CPG CPG CPG 3.2 CPG CPG 10) 3 T r dx i dt = x i + s i b i f i n a ij y i (1) j=1 y i = max(0, x i) (2) 4 c 2012 Information Processing Society of Japan

1 CPG Table 1 parameters of CPG 11 2 CPG Fig. 11 CPG of two neurons parameter value s 1, s 2 5.0 T r 1.0 T a 10.0 b 1, b 2 2.5 a 12, a 21 1.5 12 CPG Fig.

5 1 CPG Table 1 parameters of CPG 11 2 CPG Fig. 11 CPG of two neurons parameter value s 1, s T r 1.0 T a 10.0 b 1, b a 12, a CPG Fig. 12 Output of CPG T a df i dt = fi + yi (3) 13 Fig. 13 Hase s Model i x i i s i i f i i y i i T r T a x i f i b i a ij i j 2 CPG CPG 1 CPG CPG 11) 13 CPG CPG CPG 5 c 2012 Information Processing Society of Japan

6 ( 1 ) ( 2 ) IK ( 3 ) OpenSim CMC(Computed Muscle Controls) ( 4 ) FD 5.2 ( 1 ) CMC CPG CPG ( 2 ) CPG FD ( 3 ) FD 14 Fig. 14 Procedure in the proposed method Open- Sim CPG CPG 6 c 2012 Information Processing Society of Japan

Fig. 16 16 Activation of biceps femoris muscle Fig. 17 17 Activation of Long head of the biceps femoris muscle 15 Fig.

7 Fig Activation of biceps femoris muscle Fig Activation of Long head of the biceps femoris muscle 15 Fig. 15 Generated basic walk motion 1) CG-ARTS (2009). 2) Wei, X., Min, J. and Chai, J.: Physically-valid Statistical Motion Models for Hu- 7 c 2012 Information Processing Society of Japan

8 man Motion Generation, ACM Transactions on Graphics, Vol.30, No.3 (2011). 3) GA (2011). 4) Delp, S.L., Anderson, F.C., Arnold, A.S., Loan, P., Habib, A., John, C.T. and et al: OpenSim: open-source software to create and analyze dynamic simulations of movement, IEEE Trans. Biomed. Eng., Vol.54, No.11, pp (2007). 5) Simbios: OpenSim Developer s Guide, Simbios (online), available from (accessed ). 6) Winters, J.M.: Hill-based muscle models: A systems engineering perspective, Multiple Muscle Systems: Biomechanics and Movement Organization, chapter 5, pp , Springer-Verlag (1990). 7) Schutte, L.M., Rodgers, M.M., Zajac, F.E. and Glaser, R.M.: Improving the efficacy of electrical stimulation-induced leg cycle ergometry: an analysis based on a dynamic musculoskeletal model, IEEE Trans. Rehabilitation Eng., Vol.1, No.2, pp (1993). 8) Zajac, F.E.: Muscle and tendon: Properties, models, scaling, and application to biomechanics and motor control, CRC Critical Rev. in Biomed., Vol.17, No.4, pp (1989). 9) Vol.44, No.5, pp (2000). 10) Matsuoka, K.: Mechanisms of Frequency and Pattern Control in the Neural Thythm Generators, Biological Cybemetics, Vol.56, No.5-6, pp (1987). 11) Hase, K. and Yamazaki, N.: Computer Simulation Study of Human Locomotion with a Three-Dimensional Entire-Body Neuro-Musculo-Skeletal Model, The Japan Society of Mechanical Engineers, Vol.45, No.4, pp (2002). 8 c 2012 Information Processing Society of Japan

Input image Initialize variables Loop for period of oscillation Update height map Make shade image Change property of image Output image Change time L

Input image Initialize variables Loop for period of oscillation Update height map Make shade image Change property of image Output image Change time L 1,a) 1,b) 1/f β Generation Method of Animation from Pictures with Natural Flicker Abstract: Some methods to create animation automatically from one picture have been proposed. There is a method that gives