Transcript Fast and Accurate Goal- Directed Motion Synthesis For Crowds Mankyu Sung

Fast and Accurate GoalDirected Motion Synthesis
For Crowds
Mankyu Sung
Lucas Kovar
Michael Gleicher
University of Wisconsin- Madison
www.cs.wisc.edu/graphics
The Goal :
Motion synthesis for crowds
High-level behaviors
(Musse 2001, Ucliney 2002, Faranc 1990,
Sung 2004, Braun 2003)
Low-level motion synthesis
Our goal
The Goal:
Motion synthesis for crowds

Problem : Constrained
motion synthesis
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Positions, Orientation, Poses,
Time duration
Orientation
Pose
Position
Target
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Requirement
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
Fast performance
Accurate meeting constraints
High quality motions
Collision avoidance
Complicated environment
Time
duration
Initial
An example
Our approach :
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Synthesize crowds one individual at a time
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Motion graphs for
low-level synthesis
(Kovar et al. ‘02, Lee et al. ‘02,
Arikan and Forsyth ’02, Gleicher
et al. ’03)
Must adapt to crowds


Individual motions must be
found very quickly
Pure discrete synthesis cannot
meet continuous constraints
Adapting Graph based
synthesis :

Two-level synthesis
• Coarse search for global path planning
• Finer search for detailed motion synthesis
• Quickly find long motions in complex
environments
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Incorporate continuous motion adjustment
• Discrete search to roughly satisfy constraints
• Additional displacements for precision
• Improves speed and accuracy
Contents
Related work
 Synthesis Algorithms
 Demos
 Limitation

Related Work (1)

Graph based motion synthesis
(e.g. Arikan 2002, Arikan 2003, Gleicher 2003, Kovar 2002,
Hue 2004, Lee 2002, Lee 2004, Reitsma 2004)
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Connecting discrete finite clips with simple
interpolation or displacement mapping
-Create new motion strictly by attaching clips
→ Hard to satisfy constraints exactly
- Do not consider crowds.
Related Work (2)
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Planning Biped Locomotion
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(Choi 2003)
Build a PRM (Probability Roadmap Method) based on
sampled footprints configurations.
Given initial and target constraint, the PRM is searched
to find a path that is able to connect with motion clips.
Motions are adjusted to meet the constraints.
-The PRM is tightly coupled with motion clips
Related Work (3)

Procedural motion synthesis
1990, Sun 2001, Boulic 2004)
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Controllable but not as realistic as motion
capture data
Motion Blending
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(Guo 1996, Park 2004, Petteré 2003)
Continuous control over trajectory
Limited and computationally costly
Crowd Modeling

(Bouvier 1997, Boulic
(Musse 2001, Ulicny 2002, Farenc 1999)
Focus on high-level behaviors
Not have constraints to satisfy
Algorithm
1.
Rough planning
1.
2.

Example
PRM query
Fine planning
1.
2.
3.
4.
Greedy search
Create seed paths
If distance > ε
 Randomly select
and replace a clip
Joining with
adjustment
Target
Obstacle
Initial
Algorithm
1.
Rough planning
1.
2.

Example
PRM query
Fine planning
1.
2.
3.
4.
Greedy search
Create seed motions
If distance > ε
 Randomly select
and replace a clip
Joining with
adjustment
Obstacle
Target
Initial
waypoints
Algorithm
1.
Rough planning
1.
2.

Example
PRM query
Fine planning
1.
2.
3.
4.
Greedy search
Create seed motions
If distance > ε
 Randomly select
and replace a clip
Joining with
adjustment
Target
Obstacle
Initial
1
2
waypoints
3
Algorithm
1.
Rough planning
1.
2.

Example
PRM query
Fine planning
1.
2.
3.
4.
Greedy search
Create seed motions
If distance > ε
 Randomly select and
replace a clip
Joining with adjustment
Target
Obstacle
Forward
Motion(Mf)
Initial
1
Backward
Motion(Mb)
2
3
Initial’
Algorithm
1.
Rough planning
1.
2.
PRM query
Fine planning
1.
2.
3.
4.
Greedy search
Create seed motions
If distance > ε
 Randomly select
and replace a clip
Joining with
adjustment
Cost function : How close are they?
C(Mf, Mb)
>ε
Forward motions
Backward motions
Compare all pair of motions and
returns minimum cost
Algorithm
1.
Rough planning
1.
2.
PRM query
Fine planning
1.
2.
3.
4.
Greedy search
Create seed motions
If distance > ε
 Randomly select
and replace a clip
Joining with
adjustment
<ε
Old Motionsc
New motions
Old Motions
Random select and
Replace a clip
Algorithm
1.
Rough planning
1.
2.

Example
PRM query
Fine planning
1.
2.
3.
4.
Greedy search
Create seed paths
If distance > ε
 Randomly select and
replace a clip
Joining with
adjustment
Target
Obstacle
Initial
Joining
waypoints
Motion adjustment
Old Motions
New motions
New motions
Old Motions
ε
The error is distributed to the both paths
Demos
Time constrained demo
 A theater
 Box delivery
 Big crowds on virtual environment

Performance results
Example
# of Duration
agent
(sec)
AVG
Time (sec)
Total
Time (sec)
Time
constrained
20
14.8
0.21
4.2
A theater
40
30.2
0.28
11.2
Box delivery
40
30.5
0.15
6
Big crowds
500
25.6
0.035
17.5
Performance results
Speed vs. ε
Speed vs.
avg. distance between characters
Limitation
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Not optimal
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Offline
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May cause some wandering effect
Need searching time
Performance depends on environment
Density of crowds affects on performance
 The environment (size and complexity)
does matter

Acknowledgement

Financial support : NSF CCR-9984506 and
CCR-0204372
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Motion donations : House of Moves
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Hyun Joon Shin for STM system