motion modeling for online locomotion synthesis
DESCRIPTION
Motion Modeling for Online Locomotion Synthesis. Taesoo Kwon and Sung Yong Shin KAIST. Outline. Motivation Related work Overview Motion analysis Motion synthesis Conclusions Future Work. Motivation. Real-time locomotion synthesis Motion rearrangement : realism - PowerPoint PPT PresentationTRANSCRIPT
Motion Modeling for Online Locomotion
Synthesis
Taesoo Kwon and Sung Yong Shin
KAIST
Outline
• Motivation
• Related work
• Overview
• Motion analysis
• Motion synthesis
• Conclusions
• Future Work
Motivation
• Real-time locomotion synthesis
• Motion rearrangement : realism
• Motion blending : efficiency and controllability
• Hybrid approach– Locomotive motion generation [PSS02, PSS04]– Rhythmic motion synthesis [KPS03]
• Premise: motion labeling
Related Work
• Motion Segmentation [Bindiganavale & Badler, 1998;Fod et al., 2002;
Kim et al., 2003]
• Motion Classification [Arikan et al., 2003;Kovar & Gleicher, 2004;Forbes & Fiu
me 2005;Mueller & Roeder 2005]
• Motion Labeling for blending [Kim et al., 2003]
Overview
motion specifications
desiredmotion
example motions
motion analysis
hierarchical motion transition graph
motion synthesis
Motion Analysis
• Issues– Motion segmentation
– Motion classification
– Graph construction
• Biomechanical observations– [Per92,Win90]
Biomechanical Observations• Center of mass trajectory
right foot left foot
walk runtransition
COMy
Motion segmentation
• Criteria for motion segmentation– Simple enough for intuitive parameterization
– Long enough to contain motion semantics
– An important motion feature should not be split
Split at every COM peak
Motion Classification
• String encoding–
• Pros– avoid troublesome time-warping
– more robust than numerical computation
M:f
Motion Classification
• Footstep patterns
(a) S (b) R (c) L (d) D (e) F FDLRS , , , ,
Motion Classification
• String Encoding (ideal case)
Motion Classification
• String Encoding (ideal case)
R D L
Motion Classification
• String Encoding (ideal case)
F R F
Motion Classification
• String Encoding (ideal case)
R D L F
Motion Classification
• String Encoding (ideal case)
Refinement
• False peak– Concatenate two motion segments
• Missing peak– Divide a motion segment into two
Graph Construction
Graph Construction
mmmmmmP avftt ,,,,
Motion Analysis Results
• O(n) – 2Ghz PC (AMD 64, 2GB memory)
– For 7.4 min locomotion, about 10 seconds
• Movie
Motion Synthesis
LDR RDL LDRF… …
Motion Synthesis
• Motion specification
• Motion parameter
Motion Sythesis
• How to calculate – Two half cycles in cyclic motion
–
• Regression analysis on
m
RL mm
RL mm
Motion Synthesis
• Motion blending : [PSS04][KG03][ACP02]
• Motion stitching : [GSKJ03]
• Motion retargeting : [SLSG01][KGS02]
Motion Synthesis Result
• 1000+ frames per second
• Movie– Path following
– Online synthesis
Conclusion
• Motion labeling based on string encodings
• Hierarchical motion transition graph
Future work
• Footstep-driven motions such as dancing and boxing