Oct 16, Fall 2006IAT 4101 Animation Low-Level behaviors Overview Keyframing Motion Capture Simulation

Oct 16, Fall 2006IAT 4102 Low-Level Behaviors  Keyframing  Motion Capture  Simulation

Oct 16, Fall 2006IAT 4103 Generating Motion  What matters? –Quality of motion appropriate for rendering style and frame rate –Controllable from UI –Controllable from AI –Personality of the animated character

Oct 16, Fall 2006IAT 4104 Keyframe Example

Oct 16, Fall 2006IAT 4105 Keyframing  Fine level of control  Quality of motion depends on skill of animator

Oct 16, Fall 2006IAT 4106 Motion Capture  Natural-looking motion  Hard to generalize motions –Registration is difficult –“Weightless” according to professional animators

Oct 16, Fall 2006IAT 4107 Motion Capture Images courtesy Microsoft Motion Capture Group

Oct 16, Fall 2006IAT 4108 Simulation (Broadly Defined)  Physics is hard to simulate  Pseudo-physics is somewhat hard  Control is very hard  Gives Generalization + Interactivity User/ AI Desired Behavior Control Forces and Torques Model Numerical Integrator Graphics State

Oct 16, Fall 2006IAT 4109 When to Use What Method?  Keyframing –Sprites and other simple animations –Non-human characters –Coarse collision detection  Motion Capture –Human figures –Subtle motions, long motions  Simulation –Passive simulations –When interactivity w/ motion is important

Oct 16, Fall 2006IAT Keyframing

Oct 16, Fall 2006IAT Keyframing

Oct 16, Fall 2006IAT Keyframe Example

Oct 16, Fall 2006IAT Keyframing  Fine level of control  Quality of motion depends on skill of animator

Oct 16, Fall 2006IAT Hand Drawn Animation -- 2D  Sketches  Pencil tests  Inking  Coloring  Digitize to sprites

Oct 16, Fall 2006IAT Computer Animation: 2D or 3D  Sketches  Models and materials  Key configurations  Playback of motion or render to sprites

Oct 16, Fall 2006IAT Keyframing  The development process: Adjust trajectory Playback motion  Parameters: –Locations –Joint angles –Shape -- flexible objects –Material properties –Camera Motion –Lighting

Oct 16, Fall 2006IAT Keyframing Interpolation  Inbetweening 1,2, ,8,9… Linear v 1,2, ,8,9… Slow in, Slow out v time

Oct 16, Fall 2006IAT Interpolation Example

Oct 16, Fall 2006IAT Key Frames

Oct 16, Fall 2006IAT Key Frames  Timeline 

Oct 16, Fall 2006IAT Inbetweening  Frame dependent (“wrong”) slow-in/out –Iterate once per frame –This is a variant on the Infinite Impulse Response (IIR) filter:

Oct 16, Fall 2006IAT Inbetweening  Frame-Independent (“right”) slow in/out –Compute acceleration a TimeT end Time of last frame

Oct 16, Fall 2006IAT Spline-driven Animation x x,y = Q(u) for u:[0,1]  Equal arc lengths  Equal spacing in u y

Oct 16, Fall 2006IAT Reparameterize Arc Length  S= A(u) = arc length  Reparam:  Find: Bisection search for a value of u where A(u) = S with a numerical evaluation of A(u) (Details in Watt & Watt)

Oct 16, Fall 2006IAT Keyframing -- Constraints  Joint limits  Position limits  Inverse kinematics

Oct 16, Fall 2006IAT Keyframing -- Constraints

Oct 16, Fall 2006IAT Coordinate Systems

Oct 16, Fall 2006IAT Kinematics  The study of motion without regard to the forces that cause it Draw graphics Specify fewer Degrees Of Freedom (DOF) More intuitive control of DOF Pull on hand Glue feet to ground

Oct 16, Fall 2006IAT Inverse Kinematics

Oct 16, Fall 2006IAT Inverse Kinematics

Oct 16, Fall 2006IAT What makes IK Hard?  Many DOF -- non-linear transcendental equations  Redundancies –Choose a solution that is “closest” to the current configuration –Move outermost links the most –Energy minimization –Minimum time

Oct 16, Fall 2006IAT IK Difficulties  Singularities –Equations are ill-conditioned near singularities –High state-space velocities for low Cartesian velocities  Goal of “Natural Looking” motion –Minimize jerk (3rd derivative)

Oct 16, Fall 2006IAT Motion Capture  What do we need to know? –X, Y, Z –Roll, Pitch, Yaw  Errors cause –Joints to come apart –Links grow/shrink –Bad contact points  Sampling Rate and Accuracy

Oct 16, Fall 2006IAT Motion Capture  Goals: –Realistic motion –Lots of different motions ( ) –Contact  Appropriate game genres –Sports –Fighting –Human characters

Oct 16, Fall 2006IAT Applications Movies, TV Video games Performance animation

Oct 16, Fall 2006IAT 41036

Oct 16, Fall 2006IAT 41037

Oct 16, Fall 2006IAT Plan out Shoots Carefully  Know needed actions ( takes/day) –Bridges between actions –Speed of actions –Starting/ending positions  Hire the right actor –Watch for idiosyncrasies in motion –Good match in proportions

Oct 16, Fall 2006IAT Sensor Placement  Place markers carefully –Capture enough information –Watch for marker movement  Check data part way through shoot  Videotape everything!

Oct 16, Fall 2006IAT 41040

Oct 16, Fall 2006IAT Technology  Numerous technologies  Record energy transfer –Light –Electromagnetism –Mechanical skeletons

Oct 16, Fall 2006IAT Technology  Passive reflection – Peak Performance Tech –Hand or semi-automatically digitized –Video –Time consuming  Issues –No glossy or reflective materials –Tight clothing –Marker occlusion by props +High frames/sec

Oct 16, Fall 2006IAT Technology  Passive reflection --Acclaim, Motion Analysis –Automatically digitized –240Hz –Not real-time, Correspondence –3+ markers/body part –2+ cameras for 3D position data

Oct 16, Fall 2006IAT Technology  Vicon Motion Systems –Retroreflective paint on reflectors –Lights on camera –Very high contrast markers

Oct 16, Fall 2006IAT Technology  Active light sources -- Optotrak –Automatically digitized –256 markers –3500 marker/sec –Real-time –Specialized cameras

Oct 16, Fall 2006IAT Technology  Electromagnetic Transducers –Ascension Flock of Birds, etc –Polhemus Fastrak, etc  Limited range/resolution –Tethered (cables to box) –Metal in environment (treadmill, Rebar!) –No identification problem –6DOFRealtime – Hz13-18 markers

Oct 16, Fall 2006IAT Technology  Exoskeleton + angle sensors –Analogous –Tethered –No identification problem –Realtime- 500Hz –No range limit- Fit –Rigid body approximation

Oct 16, Fall 2006IAT Technology  Dataglove –Low accuracy –Focused resolution  Monkey –High accuracy –High data rate –Not realistic motion –No paid actor Mechanical motion capture

Oct 16, Fall 2006IAT Technology  Technology issues –Resolution/range of motion –Calibration –Accuracy –Occlusion/Correspondence

Oct 16, Fall 2006IAT Animation Issues  Style  Scaling  Generalization

Oct 16, Fall 2006IAT Resolution  Positioning of camera

Oct 16, Fall 2006IAT Markers, Calibration  Marker Placement –Location should move rigidly with joint –Stay away from bulging muscles, loose skin –Shoulders: Skeletal motion not closely tied to skin motion  Calibration –Zero position –Fine calibration by hand

Oct 16, Fall 2006IAT Calibration  Finding Joint Locations –Move markers to joint centers Assume rigid links, rotary joints  Shoulder?

Oct 16, Fall 2006IAT Calibration  Extract best limb lengths  Use estimator to compute limb length  Minimize or reject outliers

Oct 16, Fall 2006IAT Calibration  Example estimator: –508 frames of walking –6 bad frames –Collarbone to shoulder: Hand editing: 13.3cm Estimator: 13.2cm Arithmetic mean: 14.1cm

Oct 16, Fall 2006IAT Accuracy  Marker movement  Noise in sensor readings  Skew in measurement time  Environment restrictions  Frame rate –High frame rate allows good filtering

Oct 16, Fall 2006IAT Camera Calibration  Internal camera parameters –Optical distortion of lens  External parameters –Position and orientation  Correlation between multiple cameras

Oct 16, Fall 2006IAT Model-Based Techniques  Restricted search space for markers  Dynamics (velocity integration) –No infinite accelerations  Model of behavior  Model of bodies of occlusion –Objects don’t pass through each other

Oct 16, Fall 2006IAT Scaling Animation  Contact  Movement style  Inverse kinematics

Oct 16, Fall 2006IAT Generalizating Animation  Interpolation Synthesis for Articulated Figure Motion  Wiley and Hahn  IEEE CG&A v17#6

Oct 16, Fall 2006IAT Generalizating Animation  Keyframes as constraints in a smooth deformation –Create functions by hand that warp the joint angle curves through time  Keyframe placing the ball on the racket at impact Motion Warping Witkin and Popovic, SIGGRAPH’95

Oct 16, Fall 2006IAT Motion Warping  For each joint angle curve C(t)  Cnew(t) = C(t) * A(t) + B(t)  A(t) is usually a smooth, gentle curve

Oct 16, Fall 2006IAT Generalizating Animation  Motion Editing With Spacetime Constraints –Michael Gleicher –1997 Symposium on Interactive 3D Graphics

Oct 16, Fall 2006IAT Blending Animations  Efficient Generation of Motion Transitions Using Spacetime Constraints –Rose, Guenter, Bodenheimer, Cohen –Siggraph ’96 –Uses dynamics to compute plausible paths –Blends these paths

Oct 16, Fall 2006IAT Simulation  Modeling the real world with simple physics –Realism –A set of rules –Better interactivity  Objects or Characters

Oct 16, Fall 2006IAT Passive -- No muscles or motors Active -- Internal source of energy

Oct 16, Fall 2006IAT Equations of Motion  Water  Explosions  Rigid body models

Oct 16, Fall 2006IAT Control Systems  Wide variety of behaviors  Transitions between behaviors  Controllable by AI or UI  Robust

Oct 16, Fall 2006IAT Equations of Motion

Oct 16, Fall 2006IAT Generating Motion  What matters? –Quality of motion appropriate for rendering style and frame rate –Controllable from UI –Controllable from AI –Skills of the animated character –Personality of the animated character

Oct 16, Fall 2006IAT Keyframing  Fine level of control  Quality of motion depends on skill of animator

Oct 16, Fall 2006IAT Motion Capture  Natural-looking motion  Hard to generalize motions –Registration is difficult  Often seems “weightless” – Bill Kroyer, Rhythm & Hues

Oct 16, Fall 2006IAT Simulation (Broadly Defined)  Physics is hard to simulate  Pseudo-physics is somewhat hard  Control is very hard  Gives Generalization + Interactivity User/ AI Desired Behavior Control Forces and Torques Model Numerical Integrator Graphics State

Oct 16, Fall 2006IAT When to Use What Method?  Keyframing –Sprites and other simple animations –Non-human characters –Coarse collision detection  Motion Capture –Human figures –Subtle motions, long motions  Simulation –Passive simulations –When interactivity w/ motion is important

Oct 16, Fall 2006IAT Integration of Technologies  Layering –Add hand/finger motion later –Facial animation  Use keyframing to modify data –Fix holes in data  Use motion capture to drive simulation