1.The Challenge of Interactivity - PowerPoint Presentation

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1.The Challenge of Interactivity

2.Parametric Blending -Building Blend-Spaces -Using Virtual Example Grids for Parameterization -Combined and Layered Blend-Spaces3.Comparison with Academic Research 4.Procedural Animations

Talk OverviewSlide3
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1 – Blend-Weights can be complex to calculate

2 – Blend-Weights are not intuitive 3 – Blend-Weights can give unpredictable results Is this always a problem?

When

is it a problem?

Issues with Animation BlendingSlide6

Part

2Parametric BlendingSlide7

1. What is it?

An extension of animation blending A method to create predictable blending-results2. How does it work? It uses the captured properties of a motion-clip directly It generates the blend-weights in relation to these properties3. What can we use it for?

Parametric

BlendingSlide8
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Animation

vs Parametric BlendingThe hard part is to generate Correct Blend-Weights and Natural Results!

Getting both at the same time can be an extremely difficult process Slide13

1.

Accurate Parameter Mapping2.Artist-Directed Blending3.Continuous-Control 4.Runtime Efficient 5.Memory Efficient

Conclusion:

if only one of these features is missing,

then it’s very hard to use it in game-productions.

The 5 Features

a

Parameterizer must have!Slide14

Building-Blocks for a ParameterizerSlide15

Virtual Example Grids

The Offline ProcessThe step-by-step process how to setup a

parametric group for locomotion.

Slide16

Virtual Example Grids

The Offline Process Step 1:Asset SelectionSlide17

Virtual Example Grids

The Offline Process Step 2: Parameter ExtractionSlide18

Virtual Example Grids

The Offline Process Step 3: Setup of the Blend-SpaceSlide19

Virtual Example Grids

The Offline ProcessStep 4: Blending Annotations Weird Issue:

Different combinations of

Blend-Weights

,

can give you a blended

m

otion

with

Identical

P

arameter

,

but totally different

Visual PosesSlide20

Virtual Example Grids

The Offline ProcessAdvantages of Annotations

1.

Artist Directed Blending

2.

No “Scattered Data Interpolation” Problem

3.

Continuous Control

4.

Control over Performance

5.

Simple, Precise and Easy to Debug Slide21

Virtual Example Grids

The Offline Process Step 5:Extrapolated Pseudo ExamplesSlide22

Virtual Example Grids

The Offline Process Step 6: Virtual Example GridsSlide23

Virtual Example Grids

The Runtime Process Slide24

Virtual Example Grids

The Runtime Process Step 1: Parameterization:Slide25

Virtual Example Grids

The Runtime Process Step 2: Time-WarpingSlide26

Virtual Example Grids

The Runtime Process Step 3: Pose-BlendingSlide27

Exponential Asset Explosion 1D - 3 assets for move-speed 2D - 9 assets for move-speed / turn left-right 3D - 27 assets for speed / turn left-right / uphill-downhill 4D – 27*8 assets for speed / turn left-right / uphill-downhill multiply by 8 move-directions ---------------------------------------------------------- =216 (for 1 parametric group) -This is the raw bare minimum for a full featured character, regardless of the blending method.

-Our practical maximum was 34 assets per group

Debugging Nightmare

-More then 3 dimensions are hard to visualize & debug

-Dimensionality-Problem is the

Dead End

for Parametric Blending

Curse of DimensionalitySlide28

But 3D is not enough!

-with 3D you have only 3 Parameters to control -in a game you will need much more What’s the Solutions? -build small Blend-Spaces and combine them

-or we can

layer

Blend-Spaces

Curse of DimensionalitySlide29

Combined Blend-Spaces

Our Blend-Spaces are limited to 3 dimensions But it is possible to combine small blend-spaces Slide30

The Layer Model

Types of Layered Animations - Overwrite Animations - Additive Animations - Combination of both Methods in one AssetLayered BlendingSlide31

Parametric Weapon Aiming

Parametric Blending used in LayersSlide32

Parametric Gaze-control (including eye-lids)

Parametric Blending used in LayersSlide33

We used only

small Blend-Spaces (max 3D)With combinations it was possible to control 4DWith layering it was possible to control up to 8D

Virtual Example Grids

SummarySlide34

Part

3Comparison with Academic ResearchSlide35

Techniques for a

ParameterizerSlide36

Virtual Example Grids

vsRadial Basis Functions“Verbs and Adverbs: Multidimensional motion interpolation.” by Charles Rose, Bobby Bodenheimer and Michael Cohen (1998)“Artist directed IK using RBF interpolation.” by Charles Rose, Peter-Pike Sloan and Michael Cohen (2001)Slide37

Virtual Example Grids

vsK-Nearest Neighbors“Automated extraction and parameterization of motions” by Lucas Kovar and Michael Gleicher (2004)Slide38

Combination of IK-solvers

IK-Solvers (2B, 3B & CCD-IK) generate new posesProcedural Motion WarpingTypical ApplicationsFix of Blending-ArtifactsGround Alignment Recoil

Kinematic MethodsSlide39

From RBF to VEG

1.We started with an RBF implementation -was slow, no control over blending2.We combined RBF with KNN

-faster, but now we had snaps in the motions

3.Smoothing of Blend-Weights to avoid snaps

-worked, but smoothing messed up the parameterization

4.Manual Annotation

-this fixed all issues and made SDI redundant

5

.We used VEGs to maximize performanceSlide40

Part

4Procedural AnimationsSlide41
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Just Ragdolls Ragdolls & Animation BlendingProcedural Hit-ReactionsAnimated Hit-ReactionsInverse Dynamics Physically Based AnimationsSlide43

Summary

1.Animation-Data is the foundation2.Blend-Spaces and Parametric Animations3.Annotations -Annotations to improve the motion-quality

-Annotations to eliminate the SDI problem

-Annotations to accelerate the pose-blender

-Annotations with Pseudo-Examples to save memory

4.

Virtual Example Grids

5.

Combined and Layered Blend-Spaces

6.

Procedural TechniquesSlide44

Special thanks for the Help with this Presentation:

Benjamin Block, Chris Butcher, Daniele Duri, Frieder Erdman, Ivo Zoltan Frey, Mathias Lindner, Michelle Martin Peter North, David Ramos, Sven van Soom

,

Peter Söderbaum, Alex Taube,

Karlheinz

Watemeier

,

The Best is Yet to

C

ome

You can find a more detailed comparison between different

Parametric Methods after the Q&A Slide Slide45
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The Best is Yet to Come

You can find a more detailed comparison between differentParametric Methods Slide47

Reference &

ComparisonSlide48

Accurate Parameter Mapping

Artist-Directed BlendingContinuous-Control Runtime Efficient Memory Efficient Requirements for a ParameterizerSlide49

Interpolation Synthesis

“Interpolation synthesis for articulated figure motion” by Douglas Wiley and James Hahn (1997)1.Accurate Parameter Mapping: YES (but depends mainly on the density of the grid) 2.Artist-Directed Blending: YES (but artist are forced to fill a grid with motions)3.Continuous-Control: YES 4.Run-time Efficient: YES

5.Memory Efficient:

NO

(memory requirements and the amount of assets were insane)

Regular GridSlide50

Scattered Data Interpolation (1/5)

Radial Basis Functions“Verbs and adverbs: Multidimensional motion interpolation.” by Charles Rose, Bobby Bodenheimer and Michael Cohen (1998)1.Accurate Parameter Mapping: ??? (For IK-tasks very inaccurate)2.Artist-Directed Blending: NO (In many cases blend-poses were more or less random)3.Continuous-Control: YES (RBFs are smooth)

4.Run-time Efficient:

NO

(The parameterizer was using interpolation per DOF)

5.Memory Efficient:

YES

(Only key-examples are needed)Slide51

Scattered Data Interpolation (2/5)

Cardinal Radial Basis Functions“Artist directed IK using RBF interpolation.” by Charles Rose, Peter-Pike Sloan and Michael Cohen (2001)1.Accurate Parameter Mapping: YES (precision is coming mainly from the pseudo-examples)2.Artist-Directed Blending: NO (in many cases blend-poses were more or less random)3.Continuous-Control: YES (RBFs are smooth)4.Run-time Efficient:

???

(The more pseudo-examples, the slower)

5.Memory Efficient:

???

(Depends on the amount of Pseudo-Examples)Slide52

Scattered Data Interpolation (3/5)

K-Nearest Neighbors“Automated extraction and parameterization of motions” by Lucas Kovar and Michael Gleicher (2004)1.Accurate Parameter Mapping: YES (only with enough pseudo examples)2.Artist-Directed Blending: NO (they use random sampling. The result was more or less luck)

3.Continuous-Control:

NO

(Continuous-control was impossible)

4.Run-time Efficient:

YES

(KNN is simple and fast)

5.Memory Efficient:

NO

(requires high amount if pseudo-example)Slide53

Scattered Data Interpolation (4/5)

Geostatistical Interpolation“Geostatistical Motion Interpolation” by Tomohiko Mukai and Shigeru Kuriyama (2005)1.Accurate Parameter Mapping: YES

(accurate, but not 100

%)

2.Artist-Directed Blending:

NO

(same issue as RBFs)

3.Continuous-Control:

YES

(RBFs are smooth)

4.Run-time Efficient:

NO

(

Kringing

is slower then RBFs)

5.Memory Efficient:

YES

(it is memory efficient at the cost of more CPU power)Slide54

Scattered Data Interpolation (5/5)

Virtual Example Grids1.Accurate Parameter Mapping: YES (depends on the density of the grid)2.Artist-Directed Blending: YES (annotations for interpolation and extrapolation) 3.Continuous-Control: YES 4.Run-time Efficient: YES (all you need is a simple look-up and linear blend)

5.Memory Efficient:

YES

(depends on the density of the grid)