1.The Challenge of Interactivity2.Parametric Blending -Building Blend-Spaces -Using Virtual Example Grids for Parameterization

-Combined and Layered Blend-Spaces

3.Comparison with Academic Research

4.Procedural Animations

Talk Overview

1 – Blend-Weights can be complex to calculate2 – 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 Blending

Part 2

Parametric Blending

1. What is it? An extension of animation blending

A method to create predictable blending-results

2. How does it work? It uses the captured properties of a motion-clip directly

It generates the blend-weights in relation to these properties

3. What can we use it for?

Parametric Blending

Animation vs Parametric Blending

The hard part is to generate Correct Blend-Weights and Natural Results!

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

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!

Building-Blocks for a Parameterizer

Virtual Example Grids

The Offline Process

The step-by-step process how to setup a parametric group for locomotion.

Virtual Example GridsThe Offline Process

Step 1:Asset Selection

Virtual Example GridsThe Offline Process

Step 2: Parameter Extraction

Virtual Example GridsThe Offline Process

Step 3: Setup of the Blend-Space

Virtual Example GridsThe Offline Process

Step 4: Blending Annotations

Weird Issue:Different combinations of Blend-Weights,can give you a blended motion with Identical Parameter,but totally different Visual Poses

Virtual Example GridsThe Offline Process

Advantages of Annotations1.Artist Directed Blending2.No “Scattered Data Interpolation” Problem3.Continuous Control4.Control over Performance 5.Simple, Precise and Easy to Debug

Virtual Example GridsThe Offline Process

Step 5:Extrapolated Pseudo Examples

Virtual Example GridsThe Offline Process

Step 6: Virtual Example Grids

Virtual Example Grids

The Runtime Process

Virtual Example GridsThe Runtime Process

Step 1: Parameterization:

Virtual Example GridsThe Runtime Process

Step 2: Time-Warping

Virtual Example GridsThe Runtime Process

Step 3: Pose-Blending

• Exponential Asset Explosion1D - 3 assets for move-speed2D - 9 assets for move-speed / turn left-right3D - 27 assets for speed / turn left-right / uphill-downhill4D – 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 Dimensionality

• 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 Dimensionality

Combined Blend-Spaces

•Our Blend-Spaces are limited to 3 dimensions

•But it is possible to combine small blend-spaces

• The Layer Model• Types of Layered Animations

- Overwrite Animations- Additive Animations- Combination of both Methods in one Asset

Layered Blending

• Parametric Weapon Aiming

Parametric Blending used in Layers

• Parametric Gaze-control (including eye-lids)

Parametric Blending used in Layers

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

Virtual Example GridsSummary

Part 3

Comparison with Academic Research

Techniques for a Parameterizer

Virtual Example Gridsvs

Radial 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)

Virtual Example Gridsvs

K-Nearest Neighbors

“Automated extraction and parameterization of motions” by Lucas Kovar and Michael Gleicher (2004)

Combination of IK-solvers • IK-Solvers (2B, 3B & CCD-IK) generate new poses• Procedural Motion Warping

Typical Applications• Fix of Blending-Artifacts• Ground Alignment • Recoil

Kinematic Methods

From RBF to VEG

1.We started with an RBF implementation -was slow, no control over blending

2.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 performance

Part 4

Procedural Animations

• Just Ragdolls • Ragdolls & Animation Blending• Procedural Hit-Reactions• Animated Hit-Reactions• Inverse Dynamics

Physically Based Animations

Summary1.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 Techniques

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 ComeYou can find a more detailed comparison between different

Parametric Methods after the Q&A Slide

The Best is Yet to ComeYou can find a more detailed comparison between different

Parametric Methods

Reference & Comparison

1. Accurate Parameter Mapping

2. Artist-Directed Blending

3. Continuous-Control

4. Runtime Efficient

5. Memory Efficient

Requirements for a Parameterizer

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: YES5.Memory Efficient: NO (memory requirements and the amount of assets were insane)

Regular Grid

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)

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)

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)

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)

Scattered Data Interpolation (5/5)

Virtual Example Grids

1.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)