Animation by Example

Michael Gleicherand the UW Graphics GroupUniversity of Wisconsin- Madisonwww.cs.wisc.edu/~gleicherwww.cs.wisc.edu/graphics

Animation by Example:

The Summary We can create new animations in

response to situations by adapting existing animations and piecing them together as needed.

To do this we will: Extend our work on adapting motions Present ways of putting motions together Adapt example-based synthesis to be

appropriate for interactive applications

Virtual Experiences For training, entertainment, design, … Experience a scenario

Not just a space People are a part of it

Just one part, but an interesting one

Emergency rescue training and rehearsal Crowds populating a space

Virtual Experience Applications Training

Emergency Rescure, Crowd Control Design

Evaluating populated spaces Entertainment / Commerce

Games, Shopping Malls, Chat

All need people!

The Demands of Virtual Experiences Fast Responsive Streams of Motion Realistic Attractive Detailed Directable Autonomous

The problems What do characters do?

How do they choose their actions?

How do they do it? How do we generate their movements?

What do they look like? How do we draw them?

Cut to the chase…An Example How do you make a character sneak

around?

Start with some captured motion of a person sneaking around

Synthesize a new motion of a character “sneaking” somewhere else

What did you just see? Small amount of example motion Examples of what I want

Actions Quality

Character did something different New path

Character did it the same way Preserves “style” and “quality”

How to make a Character “Sneak”? What is sneaking?

Hard to define mathematically Abstract qualities matter

Style, mood, realism, … Details matter

Feet not sliding on the floor Subtle gestures

Computer Animation 101:

How do we get motion? Create it manually (keyframing)

Common method used for film VERY talent and labor intensive

Synthesize it by procedural methods Physical simulation, or ad-hoc methods Can’t get exactly what you want

Capture it from a performer Motion Capture Animation from Observation

Use markers and special cameras Tracking + Math

Motion Capture:Optical Tracking

All examples I will show are from optical motion capture

Mocap Pipeline(Animation from Observation)

Increasingly stable technology It’s more than just pointing cameras at

somebody! Getting the observations is just one part

of the process Need to build usable representations of

motion

Plan Shoot Process Apply

Why Edit Motion? What you get is not what you want!

You get observations of the performance A specific performer A real human Doing whatever they did With the noise and “realism” of real sensors

You want animation A character Doing something And maybe doing something else…

The General Challenge Get a specific motion

From capture, keyframe, … Specific character, action, mood, …

Want something else But need to preserve original

But we don’t know what to preserve Can’t characterize motion well enough*

*This is a working assumption of my research. I’d love to be proven wrong.

Three Problems Where does X live in the data?

Where X {style, personality, emotion, …} The things to keep or add

Small artifacts can destroy realism Eye is sensitive to certain details Amazing what you can’t get away with

• Kovar, Schreiner and Gleicher, SCA ’02

How to specify what you want

How do we handlethese problems? Don’t know which details are

important! Must preserve ALL details

Since you don’t know what is important Need to understand artifacts better

Need to represent what is important

An ApproachConstraint-Based Motion Editing

Identify specific details in motions that must be preserved Constraints such as footplants

Make conservative changes to motions Things that generally don’t cause problems Add low frequencies Blends with similar motions

Re-establish constraints (solve) Avoid creating new artifacts

Band-limited adaptation High frequencies are important

Eye is sensitive to them Always signifies important events

Avoid high frequency changes Preserve existing high-frequencies Avoid adding new ones

Band limit the changes Not the resulting motions

Band-limited adaptation? Can’t look at individual frames Need to look across space and time

Popping can be worse than skating

Constraint Solutions for Editing

Spacetime (single large non-linear optimization) Gleicher ’97, Gleicher ’98, Popovic and Witkin ’99

Hierarchical Splines Lee and Shin ’99

IK + Filter Gleicher ’00

Importance-Based Shin, Lee, Gleicher and Shin ’01

IK + Blending Kovar, Schreiner and Gleicher ’02

How to use this Editing methods just need to get

close Avoid nasty artifacts (high-frequencies)

Footskate cleanup fixes many important details

Makes editing methods easier to devise and implement

Motion Editing Interactive methods Many are easy to implement Cleanup solvers

Footskate Physics (Shin et al 2003)

Alter clips Retain the length

Getting Beyond Clips Want to generate a wider range Want more control

Applications need streams of motion Dynamically generated

Applications need “long clips”

Idea: Put Clips Together New motions from pieces of old ones!

Good news: Keeps the qualities of the original (with care) Can create long and novel “streams” (keep

putting clips together) Challenges:

How to connect clips? How to decide what clips to connect?

Connecting ClipsTransition Generation Transitions between motions can be

hard

Simple method work sometimes Blends between aligned motions Cleanup footskate artifacts

Just need to know when is “sometime”

What is Similar? Factor out invariances and measure

2) Extract windows

3) Convert to point clouds4) Align point clouds and sum squared distances

1) Initial frames

An Aside: Other Invariances (Kovar&Gleicher ’03)

Different Timing

Different Curvature

Different Constraints

An easy point to miss:Motions are Made Similar

“Undo” the differences from invariances when assembling

Rigidly transform motions to connect

Motion GraphsKovar, Gleicher, Pighin ’02

Start with a database of motions, each with type and constraint information.

Goal: add transitions at opportune points.

Motion 1

Motion 2

Motion 1

Motion 2

Other Motion Graph-like projects elsewhereDiffer in details, and attention to detail

Building a Motion Graph Find Matching States in Motions

Motion Graphs

Quality: restrict transitions

Control: build walks that meet constraints

Idea: automatically add transitions within a motion database

Edge = clip

Node = choice point

Walk = motion

Automatic Graph Construction

Find many matches (opportunistic)

Good: Automatic Good: Lots of choices

Using a motion graph Any walk on the graph is a valid motion

Generate walks to meet goals Random walks (screen savers) Search to meet constraints

Other Motion Graph-like projects elsewhere Differ in details, and attention to detail

The initial example:

Building a Motion Graph

The initial example:

Using a Motion Graph

Given a path Find a motion that minimizes distance Combinatorial optimization

Synthesis by Example Graph-based approaches

Kovar et al., Lee at al., Arikan and Forsyth Good:

High quality results Flexible

Bad: Offline (need to search) Limited Precomputation

Trade some quality for performance

We’ll fix this

Motion Graph Problems Graphs built opportunistically

Leads to unstructured graphs Don’t know what’s there No control over connectivity No way to know what is close Don’t know what character can or can’t do

Lots of transitions Can’t check them all, be conservative

Why is this OK? Search the graphs for motions Look ahead to make sure we don’t

get stuck Cleanup motions as generated Plan “around” missing transitions Optimization gets close as possible

Not OK for Interactive Apps!

Snap-Together Motion

Three basic ideas Find “Hub Nodes” Pre-process motions so they “snap”

together Build small, contrived graphs

Snapable Motions What if motions matched exactly?

Match both state and derivatives Match reasonably at a larger scale

Make motions match exactly Add in displacement maps Bumps we add to motions Modify motions to common pose Compute changes at author time

Average orCommon Pose

C(1) Displacement Maps Add displacements to make motions

match in both value and derivative Displacement maps themselves are

smoother

Motion 1

Motion 2

Cut Together

Some features of Cuts Don’t need original motions

Just use displacements No database growth

Multi-way transitions Find common point for several motions

Hub Nodes Multi-way cuts yield Hub Nodes Hub nodes allow nice graphs

Contrived Graph Structure?Search: Look ahead to

get where you need to go

React: Always lots of choices. Something close to need.

Gamers use these

Semi-Automatic Graph Construction Pick set of match frames

User selects System picks “best” one

Modify motions to build hub node Check graph and transitions

Common poses for Multi-way Hubs All motions displace to one pose Pose must be consistent with all

motions Average “common” pose

Average of poses Constraints solved on frame

Must solve constraints on all motions Inconsistencies are possible

Building the Motion Graphs

Why should we care? Precompute everything but rigid

transforms = FAST!

Nodes with high connectivity = RESPONSIVE / CONTROLLABLE

Add in user interaction Build convenient graph structures Verify transitions (less conservative)

Some results Corpus of Karate Motions

Pick two best poses Map motions to game controller 12 minutes – including picking buttons

Walk Walk + Karate

20 minutes – including picking buttons

Nothing for Free Less Variety Less “Exactness” Lower Quality Transitions

Displacements not as good as blends “Big” Transitions Big changes to get multi-way

Need user intervention Pick Match Frames Verify Transitions

In Practice, heuristics work REALLY well – but no guaruntees

Are we done yet? May not have exact motions you need

Make them using blending techniques Discrete nature (choices)

Parametric “fat” arcs Better matching of dissimilar motions

New and better invariances Motion specific invariances

How do characters decide what to do?

Snap Together Motion in ActionCrowd Simulation

Snap-Together Motion Animation by Example for

Interactive applications!

User-controlled graph structures Multi-way cut transitions Pre-computed transitions

Still more to do Interactive Systems

Online generation (no search) Low-cost runtimes

Better synthesis Self-awareness (what can you do?) Parameterized motions Better goal specifications

Multiple interacting characters Crowds Characters The bigger picture

The Vision… Visual Media for Everyone ! Visual Media

Pictures / Models Animations / Interactive Environments

Everyone Not just artists Easier for artists too

The Computer Science Tools for the creation and delivery

of visual media What representations to use

Build from “real-world” data Use for synthesis

Some current projects…

Current Projects Animation (Virtual Experiences)

Motion Synthesis Character Geometry Crowds and Behavior authoring

Vascular Visualization Virtual Videography

Virtual Videography Want to record classroom lectures

And more (start with something easy) Needs to be done well

Static camera video is unwatchable Good video holds interest, guides attention

Can’t afford a professional crew Good videography is hard Intrusive

Get a computer to simulate the crew?

Virtual Videography

Cheap and unobtrusive capture

Static cameras in back of the room

synthesize

decide

analyze

capture

Virtual Videography:Media Analysis

Build a representation of what happens

Computer Vision + Regions, Attention Model

synthesize

decide

analyze

capture

Virtual Videography:Computational Cinematography

Decide what to be shown Simulate director and editor Combinatorial optimization

chooses best shot sequence

synthesize

decide

analyze

capture

Virtual Videography:

Synthesis

Simulate Other cameras Plan good camera movements Create visual effects

synthesize

decide

analyse

capture

Vascular Visualizationw/Garet Lahvis, UW Dept of Surgery

How does the vascular system develop? In the brain?

How do we help a biologist study this? How to work with visual complexity?

Not an artist Not a film maker Not even a computer scientist

Vascular VisualizationYou could see every capillary?

This is ONE of HUNDREDS of slices of this

brain!

From Lahvis Lab, UW Dept of Surgery. Image processing by UW CS Graphics Group

Vascular Visualization:

How do you look at this? Goal driven: what do biologists need to

see? Need to manage immense complexity

Artistic presentation (abstraction) Analytical tools

Build representations of the 3D structure Leverage efficient display mechanism Afford easy analysis Unlike traditional “volume” visulization

Thanks! To the UW graphics gang. Animation research at UW is sponsored by the

National Science Foundation, Microsoft, and the Wisconsin University and Industrial Relations program.

House of Moves, IBM, Alias/Wavefront, Discreet, Pixar and Intel have given us stuff.

House of Moves, Ohio State ACCAD, and Demian Gordon for data.

And to all our friends in the business who have given us data and inspiration.