Motion Graphs

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Motion Graphs

• Davey Krill
• May 3, 2006

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Overview Project 2

• MotionTestBed Framework
• Motion Graph for 2 motion clips
• Finding candidate transitions
• Selecting transition points
• Creating Transitions (linear blending)
• Goal / Testing / Results
• Project 3

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Motion Framework

• Skeleton contains all information about the
  motion clip
• Frames indicate the orientation of joints and
  root position at that time
• Joints are hierarchically related, defining their
  position relative to their parent joint and
  globally with the root joint
• Poses can be adjusted with rigid transformations
  quaternion rotation, (x,y,z) translation

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P2 Find Candidate Transitions

• Goal Find the distance between two frames
• Method Compute the minimal sum of squared
  distances between corresponding points within the
  point clouds of each frame pose
• Compute this distance for every pair of frames in
  the motion set

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P2 Select Transition Points

• The 2D distance map can have several local minima
• Need to select only those below a certain
  threshold
• Good transitions vs. high graph connectivity
  trade-off
• Finding ways to determine this threshold is one
  focus of Project 3

Sampled 2D error function (Local minima are red
dots)
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P2 Creating Transitions

• Align the second motion with the appropriate 2D
  transformation (the minimal T, x, z).
• Create the transition frames between the ith
  frame of motion 1 and jth frame of motion 2.
  This is done by linearly blending the ith to
  ithk-1 frames with the jth-k1 to jth frames.
• Use spherical linear interpolation on joint
  rotations, and linearly interpolate root positions

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P2 Testing and Results

• Goal of P2 Find transition points between two
  motions and generate linearly blended transitions
  at those points (i.e. create a 2-motion graph).
• From this 2-motion graph, generate a new motion
  clip by manually selecting a single transition
  point.
• Distance correctness 2 identical motions,
  zero-diagonal on 2D distance function
• Example two kicking motions

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P2 Two Kicking Motions

• Empirically, any transition with a distance error
  lt6.00 generated a good motion clip
• Linear blending has been omitted to better
  illustrate the transition points
• Clip72-47.bvh
• Distance error of 5.11
• Clip86-70.bvh
• Distance error of 4.87
• Clip62-102.bvh
• Distance error of 31.66
• Example of how linear blending wouldnt help
  since the direction of momentum is different
• Which brings us to

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Project 3

• Full motion graph
• Handle arbitrary number of motions
• Pruning the graph
• Testing
• Transition point metrics
• Beyond point cloud matching
• Evaluate physical state, momentum, footskate

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P3 Full Motion Graph

• Nodes are choice points connecting clips of
  motion
• Arcs between nodes are clips of motion
• Expands on 2-motion graph.
• Each new motion added to the graph searches for
  transition points with the rest of the nodes, and
  creates new arcs appropriately.

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P3 Pruning the Graph

• Goal Prune the graph such that it is possible to
  generate arbitrarily long streams of motion.
• Remove any nodes with few or no outgoing edges.
• How to handle transition points that are close
  neighbors yet still distinct local minima.
• Example (78, 57, 5.48) and (76, 51, 6.83) are
  very close transitions. How should these be
  handled? Eliminate one? Let both persist?

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P3 Testing

• Problem of generating arbitrary motion clips from
  the motion graph not generally addressed.
• Testing by having advance knowledge of where the
  good transitions are and manually walking the
  graph.
• In progress

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P3 Transition point metrics

• Problem Determining what local minima threshold
  to use when selecting transition points is
  somewhat of a hack.
• As was seen from the earlier example, there are
  cases when using the point cloud distance is not
  a good enough metric for determining transition
  points.
• Need to consider momentum, physical state
  (imagine a person jumping up and down)
• Adaptive thresholds for
• Slow motions vs. fast motions
• Familiar (walking) motions vs. strange motions
• Context of motion determines whether you want a
  low or high threshold value

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Work in Progress

• Generate examples of an arbitrarily long motion
  graph walk
• Develop better graph pruning
• Evaluating threshold-finding techniques
• Spacing out transition points
• Adaptive threshold model

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Questions?