University of Texas at Dallas
B. Prabhakaran
Computer Animation
• Main Text:
– "Computer Animation: Algorithms & Techniques“,
Rick Parent, Morgan Kaufman publishers.
– "3D Computer Graphics: A Mathematical
Introduction with OpenGL", Samuel R. Buss,
Cambridge University Press
Multimedia System and Networking Lab @ UTD Slide- 1
University of Texas at Dallas
B. Prabhakaran
Course Outline
1.
2.
3.
4.
5.
6.
Skeletons
Quaternions
Skinning
Facial Animation
Advanced Skinning
Channels & Keyframes
7.
8.
9.
10.
11.
12.
13.
Animation Blending
Inverse Kinematics
Locomotion
Particle Systems
Cloth Simulation
Collision Detection
Rigid Body Physics
Multimedia System and Networking Lab @ UTD Slide- 2
University of Texas at Dallas
B. Prabhakaran
Contact Information
B. Prabhakaran
Department of Computer Science
University of Texas at Dallas
Mail Station EC 31, PO Box 830688
Richardson, TX 75083
Email: bprabhakaran@utdallas.edu
Fax: 972 883 2349
URL: http://www.utdallas.edu/~praba
Phone: 972 883 4680
Office: ECSS 3.706
Office Hours: Tuesdays/Thursdays 10.30-11.15am
Other times by appointments through email
Announcements: Made in class and on course web page.
TA: TBA.
Multimedia System and Networking Lab @ UTD Slide- 3
University of Texas at Dallas
B. Prabhakaran
Prerequisites
• CS 2236 (CS2), CS/SE 3345 (Data structs &
Alg. analysis), Math 2418 (Linear Algebra).
• Familiarity with
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–
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–
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Vectors (dot products, cross products…)
Matrices (4x4 homogeneous transformations)
C++ or Java
Object oriented programming
Basic physics
Multimedia System and Networking Lab @ UTD Slide- 4
University of Texas at Dallas
B. Prabhakaran
Evaluation
• 1 or 2 Homeworks
• 1 Final Exam: 75 minutes or 2 hours (depending on class room
availability). Mix of MCQs and Short Questions.
• Programming Projects
Multimedia System and Networking Lab @ UTD Slide- 5
University of Texas at Dallas
B. Prabhakaran
Grading
• 70% Projects
• 5% Homeworks
• 25% Final
Multimedia System and Networking Lab @ UTD Slide- 6
University of Texas at Dallas
B. Prabhakaran
Schedule
• Final Exam: Last week of class OR As per UTD schedule
• Projects and homework(s) schedules will be announced in class and
course web page, giving sufficient time for submission.
Multimedia System and Networking Lab @ UTD Slide- 7
University of Texas at Dallas
B. Prabhakaran
Programming Projects
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No copying/sharing of code/results will be tolerated. Any instance of
cheating in projects/homeworks/exams will be reported to the University.
No copying code from the Internet.
2 individual students copying code from Internet independently: still
considered copying in the project !!
Individual projects.
Deadlines will be strictly followed for projects and homeworks
submissions.
Projects submissions through eLearning.
Demo may be needed
Multimedia System and Networking Lab @ UTD Slide- 8
University of Texas at Dallas
B. Prabhakaran
Cheating
• Academic dishonesty will be taken seriously.
• Cheating students will be handed over to Head/Dean
for further action.
• Remember: home works/projects (exams too !) are to
be done individually.
• Any kind of cheating in home works/ projects/ exams
will be dealt with as per UTD guidelines.
• Cheating in any stage of projects will result in 0 for the
entire set of projects.
Multimedia System and Networking Lab @ UTD Slide- 9
University of Texas at Dallas
B. Prabhakaran
Proposed Projects
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3 projects
Encourage you to come up with your own project proposal too
Announcements will be made soon
Use OpenGL
Possible use of Autodesk 3D Max
Or other public domain software
C/C++ mostly
Multimedia System and Networking Lab @ UTD Slide- 10
University of Texas at Dallas
B. Prabhakaran
Applications
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Special Effects (Movies, TV)
Video Games
Virtual Reality
Simulation, Training, Military
Medical
Robotics, Animatronics
Visualization
Communication
Multimedia System and Networking Lab @ UTD Slide- 11
University of Texas at Dallas
B. Prabhakaran
Computer Animation
• Kinematics
• Physics (a.k.a. dynamics, simulation,
mechanics)
• Character animation
• Artificial intelligence
• Motion capture / data driven animation
Multimedia System and Networking Lab @ UTD Slide- 12
University of Texas at Dallas
B. Prabhakaran
Animation Process
while (not finished) {
DrawEverything();
MoveEverything();
}
• Interactive vs. Non-Interactive
• Real Time vs. Non-Real Time
Multimedia System and Networking Lab @ UTD Slide- 13
University of Texas at Dallas
B. Prabhakaran
An Example
Multimedia System and Networking Lab @ UTD Slide- 14
University of Texas at Dallas
B. Prabhakaran
The process involves…
• Motion Capture (Data Acquisition)
Multimedia System and Networking Lab @ UTD Slide- 15
University of Texas at Dallas
B. Prabhakaran
Human Motion Capture
Multimedia System and Networking Lab @ UTD Slide- 16
University of Texas at Dallas
B. Prabhakaran
UTD’s Motion Capture Facility…
Multimedia System and Networking Lab @ UTD Slide- 17
University of Texas at Dallas
B. Prabhakaran
Captured 3D Motion: E.g., 1
Multimedia System and Networking Lab @ UTD Slide- 18
University of Texas at Dallas
B. Prabhakaran
Captured 3D Motion: E.g., 2
Multimedia System and Networking Lab @ UTD Slide- 19
University of Texas at Dallas
B. Prabhakaran
Motion Capture Matrix
Pelvis Joint Data:
pelvis<AX>
Frame
pelvis<AY>
pelvis<AZ>
pelvis<TX>
pelvis<TY>
pelvis<T-Z>
1
-4.62953
-36.2313
176.458
590.269
166.422
797.569
2
-4.65407
-36.2417
176.453
590.039
166.612
797.706
3
▪
▪
▪
▪
▪
▪
Multimedia System and Networking Lab @ UTD Slide- 20
University of Texas at Dallas
B. Prabhakaran
Applying Motion Data to 3d Model
Multimedia System and Networking Lab @ UTD Slide- 21
University of Texas at Dallas
B. Prabhakaran
Animated 3D Model
Multimedia System and Networking Lab @ UTD Slide- 22
University of Texas at Dallas
B. Prabhakaran
Animation: Applications &
Possibilities
• Using an Expert to Train
• Animation Toolkit
– Content Based Retrieval of 3D Models & Motions
• Networked 3D Games
– Streaming 3D Models and Motions
• Copyright / Content Protection
• Collision Detection
Multimedia System and Networking Lab @ UTD Slide- 23
University of Texas at Dallas
B. Prabhakaran
Application: Improve Your Game !
Multimedia System and Networking Lab @ UTD Slide- 24
University of Texas at Dallas
B. Prabhakaran
3D novice motion & 2D expert motion
We can get novice pitching data using motion capture system
• There are bunch of videos include expert’s pitching motion
• 2D video data has expert’s stylistic actions
• 3D motion data compensate for the incompleteness of 2D data
•
Multimedia System and Networking Lab @ UTD Slide- 25
University of Texas at Dallas
B. Prabhakaran
2D Motion Analysis
position
Motion analysis by tracking the object (e.g. right hands)
time
Multimedia System and Networking Lab @ UTD Slide- 26
University of Texas at Dallas
B. Prabhakaran
Constraint of 2D motion Analysis
3D motion capture data
2D video motion analysis data
• We need to compare the dissimilarity between 2D & 3D data
• However, 2D data from a single camera doesn’t have enough information
for comparing with 3D motion capture data
Multimedia System and Networking Lab @ UTD Slide- 27
University of Texas at Dallas
B. Prabhakaran
Reconstruction 3D from 2D
using HMM
• Calculate most probable style-path given 2D
observations
Argmax P(Q|O1O2…OT)
Red: 3D novice motion data
Blue: reconstructed 3D motion data
Multimedia System and Networking Lab @ UTD Slide- 28
University of Texas at Dallas
B. Prabhakaran
Resynthesis
• Following to the
reconstructed 3D expert
style.
Red: 3D novice motion data
Blue: reconstructed 3D motion data
Multimedia System and Networking Lab @ UTD Slide- 29
University of Texas at Dallas
B. Prabhakaran
Another Fun: 3D Tennis Game
Realistic Tennis game – Topspin (EA Sports)
Multimedia System and Networking Lab @ UTD Slide- 30
University of Texas at Dallas
B. Prabhakaran
Application of learning expert style
motion to 3D Tennis Games
• Tennis novice can learn by comparing style
with realistic professional player
• Motion capture system can capture novice’s
naïve actions (serve, swing, volley ..)
• We can build realistic professional expert’s
actions by motion resynthesis (3D motion
reconstruction from 2D video data)
Multimedia System and Networking Lab @ UTD Slide- 31
University of Texas at Dallas
B. Prabhakaran
Parallel FSM (Finite State Machine)
• Motion capture data is not high-level semantic
data (sequences, not segmented data)
• To identify “high-level action”, we prepare
neural network and Parallel FSM
• Parallel FSM is needed since human actions
happened not in a separate way
Multimedia System and Networking Lab @ UTD Slide- 32
University of Texas at Dallas
B. Prabhakaran
Behavior Modeling: Neural Network
& Parallel FSM
• Sensor layer : two input nodes which notice the object’s movement
& boolean value of range respectively.
• Control layer : works as a hidden layer
• Stand, Straight and Grab nodes (output nodes) also initial states of each
FSMs.
Multimedia System and Networking Lab @ UTD Slide- 33
University of Texas at Dallas
B. Prabhakaran
High-level behavior recognition using
Motor-graph
• To interpret low-level actions to high-level behaviors
• Motor-graph is designed with states of FSMs
• Nodes : each state , edges: state transitions
• (c) subsumed by (b) sub-graph
Multimedia System and Networking Lab @ UTD Slide- 34
University of Texas at Dallas
B. Prabhakaran
Translate into “serve” action by
Parallel FSM & Motor Graph
Locomotion FSM
A5
Head FSM
H2
A4
H1
A2
H3
H0
A0
A1
L0
L1
L2
Arm Hands FSM
• Neural Network sensors the participant’s action and hand it to FSMs
• Each FSM recognize the state-transition and draw it to motor-graph
• This action motor-graph is subsumed by “serve” minimum motor graph
translate this action as “serve” !!
Multimedia System and Networking Lab @ UTD Slide- 35
University of Texas at Dallas
B. Prabhakaran
System Architecture:
Analysis novice’s style & feedback expert-like
action
Behavior Translation
Novice’s behavior
Showing a developed
serve with user’s style
Neural Network & FSM
Motor Graph
Hands style
Matched expert’s serve
Head style
Locomotion style
Style Analysis
Multimedia System and Networking Lab @ UTD Slide- 36
University of Texas at Dallas
B. Prabhakaran
Animation Toolkit
• Animation Authoring
Through Reuse:
– Motion mapping
– Inverse kinematics
• Example:
– GET walking FROM
Andy
– GET waving FROM
Andy
– JOIN Andy.walking
WITH Andy.waving
Multimedia System and Networking Lab @ UTD Slide- 37
University of Texas at Dallas
B. Prabhakaran
Animation Authoring Toolkit
Multimedia System and Networking Lab @ UTD Slide- 38
University of Texas at Dallas
B. Prabhakaran
Animation Query Handling
• Partial Fuzzy Query Resolution:
– Primary attribute centric query resolution
• insert an animation sequence where Mickey Mouse is walking slowly
in a park with a fountain or a statue in the background
– Heuristics for retrieving top k objects
• Maximal grade based approach
• Maximal attributes based approach
• Threshold algorithm
– Decent precision and recall shown.
• “Partial Fuzzy Query Resolution for Animation
Authoring” (Phanivas Kotharu, MS Thesis, UTD).
Multimedia System and Networking Lab @ UTD Slide- 39
University of Texas at Dallas
B. Prabhakaran
Animation Toolkit
Capture
Compression
Metadata based Query
Indexing
Query / Data
Processor
Network
………..
………..
………..
Query by Example
Index Tree
Deliverable Data
Multimedia System and Networking Lab @ UTD Slide- 40
University of Texas at Dallas
B. Prabhakaran
Shape Analysis of 3d models
• Applications
- Categorization of shapes
- Indexing techniques of 3d models
- Querying techniques for 3d model database
Ultimate goal: Content based 3d model search
Multimedia System and Networking Lab @ UTD Slide- 41
University of Texas at Dallas
B. Prabhakaran
Streaming 3D Games Over the Internet
3D Streaming
Server
Network
Rendering Client
Multimedia System and Networking Lab @ UTD Slide- 42
University of Texas at Dallas
B. Prabhakaran
3D Model Streaming
• Advantages:
– 1 Base Mesh + M
Refinements =
Original Mesh
– Bandwidth Friendly
• Drawbacks:
– Intolerant to
Transmission errors
– Not friendly for
Real Time 3D
Streaming
1.
Base Mesh
Faces: 4281
Vertices: 2249
Size:131KB
Mesh after 5 Batches
2.
Faces: 23675
Vertices: 11946
Size: 748KB
3.
Original Mesh
Faces: 69451
Vertices: 35947
Size: 3MB
Multimedia System and Networking Lab @ UTD Slide- 43
University of Texas at Dallas
B. Prabhakaran
Content Protection of 3D models and
MoCap Data
• 3D models and MoCap Data
- Commercial value (“money”)
- Requires lot of human effort
• Tampering and piracy of data:
– loss of information, with ultimate loss of time
and money.
– Faulty training & education
• How do we do content protection to avoid
piracy and tampering ?
Multimedia System and Networking Lab @ UTD Slide- 44
University of Texas at Dallas
B. Prabhakaran
Tamper Proofing Game Data
Secure data used for driving the game (different from
outcome data)
• Tamper proofing
– Detect (and possibly correct) attacks on data
• Water marking (more to do with copyrighting)
• Focus both on 3D models, motion, apart from other
data
Multimedia System and Networking Lab @ UTD Slide- 45
University of Texas at Dallas
B. Prabhakaran
Collision Detection
 Authoring operations may lead to unintentional collisions.
 Collision detection: alert authors on possible collision
detection and suggest possibilities for avoiding them.

Previously used approaches for Collision Detection can be
classified into 3 categories.
 Cell Based
 Tree Based
 Bounding Object Based
Multimedia System and Networking Lab @ UTD Slide- 46
University of Texas at Dallas
B. Prabhakaran
Cell-based Approach
 Divide the entire search space into a number of cells and a
collision possibility is triggered if two objects come in one
cell.

Disadvantages:
• High memory usage.
• Tough to correctly determine the size of each cell.
• Too small a cell: objects occupying many cells and hence more
collision tests.
• Too big a cell: unnecessary tests being carried out.
Multimedia System and Networking Lab @ UTD Slide- 47
University of Texas at Dallas
B. Prabhakaran
The motion of Object A causes a rippling effect on
Object C after colliding with Object B. The possibility of
Collision of Object C can be detected when the bounding
sphere of Object A encompasses C. This helps in Early
detection of the Collision.
Multimedia System and Networking Lab @ UTD Slide- 48
University of Texas at Dallas
B. Prabhakaran
Course Outline
1.
2.
3.
4.
5.
6.
Skeletons
Quaternions
Skinning
Facial Animation
Advanced Skinning
Channels & Keyframes
7.
8.
9.
10.
11.
12.
13.
Animation Blending
Inverse Kinematics
Locomotion
Particle Systems
Cloth Simulation
Collision Detection
Rigid Body Physics
Multimedia System and Networking Lab @ UTD Slide- 49