Talk Overview
1.The Challenge of Interactivity
2.Parametric Blending
-Building Blend-Spaces
-Using Virtual Example Grids for Parameterization
-Combined and Layered Blend-Spaces
3.Comparison with Academic Research
4.Procedural Animations
Issues with Animation Blending
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?
Part 2
Parametric Blending
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?
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
The 5 Features a Parameterizer must have!
1.Accurate Parameter Mapping
2.Artist-Directed Blending
3.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.
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 Grids
The Offline Process
Step 1:
Asset Selection
Virtual Example Grids
The Offline Process
Step 2:
Parameter Extraction
Virtual Example Grids
The Offline Process
Step 3:
Setup of the Blend-Space
Virtual Example Grids
The 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 Grids
The Offline Process
Advantages 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
Virtual Example Grids
The Offline Process
Step 5:
Extrapolated Pseudo Examples
Virtual Example Grids
The Offline Process
Step 6:
Virtual Example Grids
Virtual Example Grids
The Runtime Process
Virtual Example Grids
The Runtime Process
Step 1:
Parameterization:
Virtual Example Grids
The Runtime Process
Step 2:
Time-Warping
Virtual Example Grids
The Runtime Process
Step 3:
Pose-Blending
Curse of Dimensionality
• 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 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
Combined Blend-Spaces
•Our Blend-Spaces are limited to 3 dimensions
•But it is possible to combine small blend-spaces
Layered Blending
• The Layer Model
• Types of Layered Animations
- Overwrite Animations
- Additive Animations
- Combination of both Methods in one Asset
Parametric Blending
used in Layers
• Parametric Weapon Aiming
Parametric Blending
used in Layers
• Parametric Gaze-control (including eye-lids)
Virtual Example Grids
Summary
• 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
Part 3
Comparison with Academic Research
Techniques for a Parameterizer
Virtual Example Grids
vs
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 Grids
vs
K-Nearest Neighbors
“Automated extraction and parameterization of motions”
by Lucas Kovar and Michael Gleicher (2004)
Kinematic Methods
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
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
Physically Based Animations
•
•
•
•
•
Just Ragdolls
Ragdolls & Animation Blending
Procedural Hit-Reactions
Animated Hit-Reactions
Inverse Dynamics
Summary
1.Animation-Data is the foundation
2.Blend-Spaces and Parametric Animations
3.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 Come
You can find a more detailed comparison between different
Parametric Methods after the Q&A Slide
The Best is Yet to Come
You can find a more detailed comparison between different
Parametric Methods
Reference & Comparison
Requirements for a Parameterizer
1.
2.
3.
4.
5.
Accurate Parameter Mapping
Artist-Directed Blending
Continuous-Control
Runtime Efficient
Memory Efficient
Regular Grid
Interpolation Synthesis
“Interpolation synthesis for articulated figure motion”
by Douglas Wiley and James Hahn (1997)
1.Accurate Parameter Mapping:
2.Artist-Directed Blending:
3.Continuous-Control:
4.Run-time Efficient:
5.Memory Efficient:
YES (but depends mainly on the density of the grid)
YES (but artist are forced to fill a grid with motions)
YES
YES
NO (memory requirements and the amount of assets were insane)
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:
2.Artist-Directed Blending:
3.Continuous-Control:
4.Run-time Efficient:
5.Memory Efficient:
??? (For IK-tasks very inaccurate)
NO (In many cases blend-poses were more or less random)
YES (RBFs are smooth)
NO (The parameterizer was using interpolation per DOF)
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:
2.Artist-Directed Blending:
3.Continuous-Control:
4.Run-time Efficient:
5.Memory Efficient:
YES (precision is coming mainly from the pseudo-examples)
NO (in many cases blend-poses were more or less random)
YES (RBFs are smooth)
??? (The more pseudo-examples, the slower)
??? (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:
2.Artist-Directed Blending:
3.Continuous-Control:
4.Run-time Efficient:
5.Memory Efficient:
YES (only with enough pseudo examples)
NO (they use random sampling. The result was more or less luck)
NO (Continuous-control was impossible)
YES (KNN is simple and fast)
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:
2.Artist-Directed Blending:
3.Continuous-Control:
4.Run-time Efficient:
5.Memory Efficient:
YES
NO
YES
NO
YES
(accurate, but not 100%)
(same issue as RBFs)
(RBFs are smooth)
(Kringing is slower then RBFs)
(it is memory efficient at the cost of more CPU power)
Scattered Data Interpolation (5/5)
Virtual Example Grids
1.Accurate Parameter Mapping:
2.Artist-Directed Blending:
3.Continuous-Control:
4.Run-time Efficient:
5.Memory Efficient:
YES (depends on the density of the grid)
YES (annotations for interpolation and extrapolation)
YES
YES (all you need is a simple look-up and linear blend)
YES (depends on the density of the grid)