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Interactive Control of Avatars Animated with Human Motion Data. Jehee Lee Carnegie Mellon University Seoul National University. Jinxiang Chai Carnegie Mellon University. Paul S. A. Reitsma Brown University. Jessica K. Hodgins Carnegie Mellon University. Nancy S. Pollard Brown University.

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interactive control of avatars animated with human motion data

Interactive Control of Avatars Animated with Human Motion Data

Jehee Lee

Carnegie Mellon University

Seoul National University

Jinxiang Chai

Carnegie Mellon University

Paul S. A. Reitsma

Brown University

Jessica K. Hodgins

Carnegie Mellon University

Nancy S. Pollard

Brown University

avatars controllable responsive animated characters
Avatars:Controllable, ResponsiveAnimated Characters
  • Realistic behavior
  • Non-trivial environment
  • Intuitive user interface
interactive avatar control
Interactive Avatar Control
  • How to create a rich set of behaviors ?
  • How to direct avatars ?
  • How to animate avatar motion ?

Motion

Database

Animate

Avatars

Motion

Sensor

Data

Preprocess

Search

User

Interface

related work probabilistic statistical models
Related Work(Probabilistic/Statistical Models)

Statistical models

  • Bradley & Stuate 97
  • Brand & Hertzmann 00
  • Pullen & Bregler 00
  • Bowden 00
  • Galata, Johnson & Hogg 01
  • Li, Wang & Shum 02

Search and playback original motion data

  • Molina-Tanco & Hilton 00
  • Pullen & Bregler 02
  • Arikan & Forsyth 02
  • Kovar, Gleicher & Pighin 02
  • This work
motion database
Motion Database

In video games

  • Many short, carefully planned, labeled motion clips
  • Manual processing
slide6

Walk Cycle

Start

Stop

Left Turn

Right Turn

motion database7
Motion Database

Our approach

  • Extended, unlabeled sequences
  • Automatic processing
re sequence
Re-sequence

Motion capture region

Virtual environment

Sketched path

Obstacles

re sequence11
Re-sequence

Motion capture region

Virtual environment

data acquisition
Data Acquisition

“Polesand Holes” rough terrain

unstructured input data
Unstructured Input Data

A number of motion clips

  • Each clip contains many frames
  • Each frame represents a pose
unstructured input data15
Unstructured Input Data

Connecting transition

  • Between similar frames
distance between frames
Distance between Frames

Weighted differences

of joint angles

Weighted differences

of joint velocities

pruning transitions

i

j

Pruning Transitions

Reduce storage space

  • O(n^2) will beprohibitive

Better quality

  • Pruning “bad” transitions

Efficient search

  • Sparse graph
pruning transition
Pruning Transition
  • Contact state:Avoid transition to dissimilar contact state
  • Likelihood: User-specified threshold
  • Similarity: Local maxima
  • Avoid dead-ends: Strongly connected components
graph search
Graph Search

Best-first graph traversal

  • Path length is bounded
  • Fixed number of frames at each frame

Comparison to global search

  • Intended for interactive control
  • Not for accurate global planning
comparison to real motion
Comparison to Real Motion

Environment with physical obstacles

global vs local coordinates
Global vs. Local Coordinates

Global, fixed,

object-relative

coordinates

Local, moving,

body-relative

coordinates

user interface
User Interface

In maze and terrain environments

  • Sketch interface was effective
user interface25
User Interface

In playground

  • A broader variety of motions are available
choice interface
Choice Interface

What is available in database ?

  • Provide with several options
  • Select among available behaviors
clustering
Clustering

A

A

C

B

C

A

D

E

E

D

cluster tree

A

A

B

C

D

Cluster Tree

Three possible actions:ABA, ABC, ABD

A

A

C

B

C

A

D

E

E

D

cluster forest

A

A

B

C

D

Cluster Forest

B

A

C

C

E

A

A

C

B

C

A

D

E

E

D

A

C

D

B

D

performance interface
Performance Interface

Motion

Database

Search

Vision-based

Interface

search
Search

3 sec

Video buffer

A

A

C

B

C

A

D

E

E

D

search38

A

A

B

D

Search

3 sec

A

A

C

B

C

A

D

E

E

D

summary
Summary

Graph representation

  • Flexibility in motion

Cluster forest

  • A map for avatar’s behavior

User interfaces

future work
Future Work

Body-relative vs. object-relative

  • Assemble objects in new configurations
  • Interactions among avatars

Evaluate user interface

  • User test for effectiveness

Combine with existing techniques

  • Motion editing and style modifications
acknowledgements
Acknowledgements

Thank

  • All of our motion capture subjects
  • Rory and Justin Macey

Support

  • NSF

Project web page

http://graphics.snu.ac.kr/~jehee/Avatar/avatar.htm