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1.The Challenge of Interactivity 2.Parametric Blending -Building Blend-Spaces -Building Blend-Spaces -Using Virtual Example Grids for Parameterization.

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Presentation on theme: "1.The Challenge of Interactivity 2.Parametric Blending -Building Blend-Spaces -Building Blend-Spaces -Using Virtual Example Grids for Parameterization."— Presentation transcript:

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2 1.The Challenge of Interactivity 2.Parametric Blending -Building Blend-Spaces -Building Blend-Spaces -Using Virtual Example Grids for Parameterization -Using Virtual Example Grids for Parameterization -Combined and Layered Blend-Spaces 3.Comparison with Academic Research 4.Procedural Animations Talk Overview

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5 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? Is this always a problem? When is it a problem? When is it a problem? Issues with Animation Blending

6 Part 2 Parametric Blending

7 1. What is it? An extension of animation blending An extension of animation blending A method to create predictable blending-results A method to create predictable blending-results 2. How does it work? It uses the captured properties of a motion-clip directly It uses the captured properties of a motion-clip directly It generates the blend-weights in relation to these properties It generates the blend-weights in relation to these properties 3. What can we use it for? Parametric Blending

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12 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

13 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. The 5 Features a Parameterizer must have!

14 Building-Blocks for a Parameterizer

15 Virtual Example Grids The Offline Process The step-by-step process how to setup a parametric group for locomotion.

16 Virtual Example Grids The Offline Process Step 1: Asset Selection

17 Virtual Example Grids The Offline Process Step 2: Parameter Extraction

18 Virtual Example Grids The Offline Process Step 3: Setup of the Blend-Space

19 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

20 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

21 Virtual Example Grids The Offline Process Step 5: Extrapolated Pseudo Examples

22 Virtual Example Grids The Offline Process Step 6: Virtual Example Grids

23 The Runtime Process

24 Virtual Example Grids The Runtime Process Step 1: Parameterization:

25 Virtual Example Grids The Runtime Process Step 2: Time-Warping

26 Virtual Example Grids The Runtime Process Step 3: Pose-Blending

27 Exponential Asset Explosion 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. -This is the raw bare minimum for a full featured character, regardless of the blending method. -Our practical maximum was 34 assets per group -Our practical maximum was 34 assets per group Debugging Nightmare Debugging Nightmare -More then 3 dimensions are hard to visualize & debug -More then 3 dimensions are hard to visualize & debug -Dimensionality-Problem is the Dead End for Parametric Blending -Dimensionality-Problem is the Dead End for Parametric Blending Curse of Dimensionality

28 But 3D is not enough! 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? What’s the Solutions? -build small Blend-Spaces and combine them -or we can layer Blend-Spaces Curse of Dimensionality

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

30 The Layer Model The Layer Model Types of Layered Animations Types of Layered Animations - Overwrite Animations - Additive Animations - Combination of both Methods in one Asset Layered Blending

31 Parametric Weapon Aiming Parametric Weapon Aiming Parametric Blending used in Layers

32 Parametric Gaze-control (including eye-lids) Parametric Gaze-control (including eye-lids) Parametric Blending used in Layers

33 We used only small Blend-Spaces (max 3D) We used only small Blend-Spaces (max 3D) With combinations it was possible to control 4D With combinations it was possible to control 4D With layering it was possible to control up to 8D With layering it was possible to control up to 8D Virtual Example Grids Summary

34 Part 3 Comparison with Academic Research

35 Techniques for a Parameterizer

36 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)

37 Virtual Example Grids vs K-Nearest Neighbors “Automated extraction and parameterization of motions” by Lucas Kovar and Michael Gleicher (2004)

38 Combination of IK-solvers IK-Solvers (2B, 3B & CCD-IK) generate new poses IK-Solvers (2B, 3B & CCD-IK) generate new poses Procedural Motion Warping Procedural Motion Warping Typical Applications Fix of Blending-Artifacts Fix of Blending-Artifacts Ground Alignment Ground Alignment Recoil Recoil Kinematic Methods

39 From RBF to VEG 1.We started with an RBF implementation -was slow, no control over blending -was slow, no control over blending 2.We combined RBF with KNN -faster, but now we had snaps in the motions -faster, but now we had snaps in the motions 3.Smoothing of Blend-Weights to avoid snaps -worked, but smoothing messed up the parameterization -worked, but smoothing messed up the parameterization 4.Manual Annotation -this fixed all issues and made SDI redundant -this fixed all issues and made SDI redundant 5.We used VEGs to maximize performance

40 Part 4 Procedural Animations

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42 Just Ragdolls Just Ragdolls Ragdolls & Animation Blending Ragdolls & Animation Blending Procedural Hit-Reactions Procedural Hit-Reactions Animated Hit-Reactions Animated Hit-Reactions Inverse Dynamics Inverse Dynamics Physically Based Animations

43 Summary 1.Animation-Data is the foundation 2.Blend-Spaces and Parametric Animations 3.Annotations -Annotations to improve the motion-quality -Annotations to improve the motion-quality -Annotations to eliminate the SDI problem -Annotations to eliminate the SDI problem -Annotations to accelerate the pose-blender -Annotations to accelerate the pose-blender -Annotations with Pseudo-Examples to save memory -Annotations with Pseudo-Examples to save memory 4.Virtual Example Grids 5.Combined and Layered Blend-Spaces 6.Procedural Techniques

44 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

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46 The Best is Yet to Come You can find a more detailed comparison between different Parametric Methods

47 Reference & Comparison

48 1.Accurate Parameter Mapping 2.Artist-Directed Blending 3.Continuous-Control 4.Runtime Efficient 5.Memory Efficient Requirements for a Parameterizer

49 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 Grid

50 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)

51 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)

52 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)

53 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)

54 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)


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