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Motion Graph
Jehee Lee Seoul National University
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Motion Database
• In video games – Many short, carefully planned, labeled motion clips – Manual processing
• Motion graphs – Extended, unlabelled sequences – Automatic processing
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Maze – Sketch Interface
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Re-sequence motion frames
Motion capture region Virtual environment
Sketched path
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Re-sequence motion frames
Motion capture region Virtual environment
Sketched path
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Data Acquisition
• “Poles and Holes” rough terrain
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Terrain Navigation
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Unstructured Input Data
• A number of motion clips – Each clip contains many frames – Each frame represents a pose
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Unstructured Input Data
• Connecting transitions – Between similar frames
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Unstructured Input Data
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Pruning Transitions
• Reduce storage space – O(n^2) will be prohibitive
• Better quality – Pruning “bad” transitions
• Efficient search – Sparse graph
i
j
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Pruning Rules
• Contact state: Avoid transition to dissimilar contact state
• Likelihood: User-specified threshold
• Similarity: Local maxima
• Avoid dead-ends: Strongly connected components
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Comparison to Real Motion
• Environment with Physical Obstacles
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Comparison to Real Motion
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Data-Driven Keyframing
• Inbetweening – Pose – Location (or continuous path) – Pose and Time – Pose and Location – Pose and Time and Location
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Data-Driven Keyframing
• Criterion – Pose Accuracy
• In general, there is no exact match • Motion editing techniques can be used to
achieve exact interpolation
– Minimum time • # of frames traversed through motion graph
– Minimum distance • Distance traversed by the center of gravity
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Search Techniques
• Graph traversal – Pose-to-pose inbetweening
• Branch-and-Bound – A*-algorithm – Limited horizon on-line algorithm
• Dynamic programming – Pose-Time space – Pose-Location space
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Graph Traversal
• Pose-to-pose inbetweening
Keyframe1 Keyframe3Keyframe2
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Graph Traversal
• Pose-to-pose inbetweening
Keyframe1 Keyframe3Keyframe2
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Dynamic Programming• Pose and time
Pose j
Key t1
Key t2
Key t3
Key t4
( ) ),(),ˆ()1,ˆ(min),(ˆ
kjKjjTtjDtjD tj++−=
Keyframe match costTransition cost
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Branch-and-Bound Search
Motion capture region Virtual environment
Sketched path
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Branch-and-Bound Search
• A*-algorithm – Optimal search techniques – Infinite horizon – Competitive with discrete state space – Slow and memory intensive in continuous domain
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Branch-and-Bound Search
• On-line search – Non-optimal – Limited horizon – Much faster than A* algorithm – Better suited for on-line applications
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Dynamic Programming
• Pose and (fixed) location – Randomly sample locations – min_full.avi (Choi, Lee, Shin 2003)
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Dynamic Programming
• Pose and (relative) location – Precompute and tabulate expected future rewards – mocap_boxer.mov (Lee and Lee 2004)
),( esV
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