Motion Level of Detail: A Character Simplification Method in a Crowd Scene

1
Motion Level of Detail A Character
Simplification Method in a Crowd Scene

• Junghyun Ahn
• VR Lab. KAIST

Kwangyun Wohn VR Lab. KAIST
2
Contents

• Research topics on Crowd Animation
• What is Motion LoD?
• Related Works
• Motion Dependent vs. Independent
• Joint Posture Clustering (JPC)
• Posture Difference
• Clustering Algorithm
• Global Optimization
• Agglomerative Clustering
• Motion Retargeting
• Conclusion

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Crowd
Group
G
G
G
Individual
I
I
I
G
G
Sensor - Get environment - Collision check
I
I
Behavior Routines - Transition
Behavior Rules
Flow Analysis
Motor Controller - Animate - Simplification
User
Group Control
Individual Control
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Classification

• Scene Reality
• Crowd Modeling BOUVIER 97MUSSE 98 01
• Flow Analysis SHAW 70HELBING 00 SHEKHAR 02
• Behavioral Rules TU 94 MUSSE 97REYNOLDS 99
• Collision Avoidance REYNOLDS 87BROGAN 97KIM
  03
• Motion Transition REYNOLDS 87TU 94KIM 03
• Performance
• Image-based Technique AUBEL 00TECCHIA 00
• Motion Simplification CARLSON 97 AHN 04
• Interactivity
• Individual Control BLUMBERG 95PERLIN 96MUSSE
  99
• Group Control TU 94MUSSE 99REYNOLDS 00

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What is Motion LoD?
Skeleton simplification
LoD control
Crowd Scene
Select motion
Worldtransform
Select character
Mapping motion to character
Geometry simplification
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Motivation

• Recent trends in real-time crowd animation
• Characters in the scene ?
• Joints per character ?
• Mesh complexity ?
• Need simplification of each character
• Simplify skeleton (joint)
• Simplify geometry (mesh)
• LoD control

Need preprocessing
Enhance performance
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Simple Experiment
Rendering
DataLoading
Computation ratio at the run-time
Rendering(58)
PostureTransform(23)
Skin-ning(11)
Etc.(8)
Environment - 1000 Characters - Each Character
1000 tris 50 joints
Scene Generation(42)
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Related Works

• Skeleton simplification
• CARLSON 97
• Adjust level of simulation
• POPOVIC 99
• Skeleton simplification for motion blending
• Character motion is not concerned
• Geometry simplification
• TECCHIA 00
• Image sequence using imposter
• AUBEL 00
• Multi-resolution geometric model / Imposter
• Good performance
• Lack of controllability

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Skeleton SimplificationMotion Dependent vs.
Independent

• Motion Dependent Approach
• Dynamic skeleton
• Simplify skeleton using motion analysis
• Skeleton structure change each frame
• Motion Independent Approach
• Static skeleton
• Simplify skeleton without motion analysis
• Pre simplify skeleton

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Skeleton SimplificationMotion Dependent vs.
Independent
Advantage
Disadvantage

• Similarity Low
• Low Performance (Simple Motion)
• Level Discrete

• Motion Smooth
• High Performance (Complex Motion)
• Computation Low

MotionIndependent

• Similarity High
• High Performance (Simple Motion)
• Level Continuous

• Motion Rough
• Low Performance (Complex Motion)
• Computation High

MotionDependent
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Motion Dependent Approach Joint Posture
Clustering (JPC)

• Skeleton simplification
• Motion Dependent Approach
• Posture Difference
• E Epos Eori
• Clustering Algorithm
• Difference Map (Clustering Table)
• Level Control
• Distance
• Geometry simplification
• Progressive mesh applied in non-shared vertex

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Posture Difference
Position Difference between two Posture
Joint j
Joint c
Posture of t
Joint c1
Joint c2
Posture of tref
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Posture Difference
Orientation Difference between two Posture
Joint j
Joint c
Posture of t
Joint c1
Joint c2
Final Posture Difference
Posture of tref
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Difference Map of Joint j (Clustering Table)
Similar posture(Low Ej(t, tref) value)
t
Different posture(High Ej(t, tref) value)
tref
Element Ej(t, tref)
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Difference Map of Joints
Similar posture(Low Ej(t, tref) value)
RForeArm
Different posture(High Ej(t, tref) value)
Spine (near root)
LFoot (near leaf)
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Clustering AlgorithmGet clustered posture
ClusteredPosture
Get Clustered Posture Find each Rectanglewhere
1. Top-left coordinate (i, j) ? ij2. Width
Height3. At least all elements of a row is
lower than threshold
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Clustering AlgorithmGet the key postures

• Key posture generation
• Minimum row ? key posture of cluster

ClusteredPosture
Difference Map
Key Posture
Clustered Posture
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Clustered Result
Error 10.0, Clusters 8
Error 30.0, Clusters 5
Error 50.0, Clusters 3
Error 90.0, Cluster 1
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Motion DanceJoints 28
Motion Push OverJoints 80
Original
E 5.0
E 50.0
E 100.0
E 300.0
Motion SmashJoints 80
Motion Look AroundJoints 80
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Average Simplified Joints
Simp. Rate () per frame
28 joints
80 joints
80 joints
80 joints
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JPC Applied in a Crowd Scene
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Motion Independent ApproachModified Structure
Retargeting

• Ongoing work
• Pre simplify base skeleton structure
• Agglomerative Clustering
• Retarget motion to simplified structure
• Nonlinear Optimization

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Clustering Method

• Non-hierarchical Clustering
• K-means Clustering
• Hierarchical Clustering
• Agglomerative Clustering

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Skeleton Simplification using Agglomerative
Clustering
Original
5
10
15
20
25
30
45
40
60
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Optimization Process
Original Motion
Morg
Number of Joints m
Clustered Base Structure
j lt m
Modified Motion
Number of Joints j
Mmod
Number of Joints j
Minimize(Mmod - Msimp) Subject to Both motion
have same root position
Optimization
Retargeting Result
Msimp
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20 simplified
40 simplified
White joints Original StructureBlue joints
Retargeted Result
50 simplified
60 simplified
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Conclusion Future Works

• Motion Level-of-Detail
• Enhance real-time performance of the crowd scene
• Skeleton simplification
• Motion dependent vs. independent
• Future Works
• Skeleton Simplification Considering continuity
  of animation
• Geometry Simplification Embed mesh to the
  skeleton
• LoD control Apply various visual information