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

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Presentation on theme: "Oct 16, Fall 2006IAT 4101 Animation Low-Level behaviors Overview Keyframing Motion Capture Simulation."— Presentation transcript:

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

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

3 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

4 Oct 16, Fall 2006IAT 4104 Keyframe Example

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

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

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

8 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

9 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

10 Oct 16, Fall 2006IAT 41010 Keyframing

11 Oct 16, Fall 2006IAT 41011 Keyframing

12 Oct 16, Fall 2006IAT 41012 Keyframe Example

13 Oct 16, Fall 2006IAT 41013 Keyframing  Fine level of control  Quality of motion depends on skill of animator

14 Oct 16, Fall 2006IAT 41014 Hand Drawn Animation -- 2D  Sketches  Pencil tests  Inking  Coloring  Digitize to sprites

15 Oct 16, Fall 2006IAT 41015 Computer Animation: 2D or 3D  Sketches  Models and materials  Key configurations  Playback of motion or render to sprites

16 Oct 16, Fall 2006IAT 41016 Keyframing  The development process: Adjust trajectory Playback motion  Parameters: –Locations –Joint angles –Shape -- flexible objects –Material properties –Camera Motion –Lighting

17 Oct 16, Fall 2006IAT 41017 Keyframing Interpolation  Inbetweening 1,2,3 4 5 6 7,8,9… Linear v 1,2,3 4 5 6 7,8,9… Slow in, Slow out v time

18 Oct 16, Fall 2006IAT 41018 Interpolation Example

19 Oct 16, Fall 2006IAT 41019 Key Frames 123123

20 Oct 16, Fall 2006IAT 41020 Key Frames  Timeline  1 2 3 3 1

21 Oct 16, Fall 2006IAT 41021 Inbetweening  Frame dependent (“wrong”) slow-in/out –Iterate once per frame –This is a variant on the Infinite Impulse Response (IIR) filter:

22 Oct 16, Fall 2006IAT 41022 Inbetweening  Frame-Independent (“right”) slow in/out –Compute acceleration a TimeT end Time of last frame

23 Oct 16, Fall 2006IAT 41023 Spline-driven Animation x x,y = Q(u) for u:[0,1]  Equal arc lengths  Equal spacing in u y

24 Oct 16, Fall 2006IAT 41024 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)

25 Oct 16, Fall 2006IAT 41025 Keyframing -- Constraints  Joint limits  Position limits  Inverse kinematics

26 Oct 16, Fall 2006IAT 41026 Keyframing -- Constraints

27 Oct 16, Fall 2006IAT 41027 Coordinate Systems

28 Oct 16, Fall 2006IAT 41028 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

29 Oct 16, Fall 2006IAT 41029 Inverse Kinematics

30 Oct 16, Fall 2006IAT 41030 Inverse Kinematics

31 Oct 16, Fall 2006IAT 41031 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

32 Oct 16, Fall 2006IAT 41032 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)

33 Oct 16, Fall 2006IAT 41033 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

34 Oct 16, Fall 2006IAT 41034 Motion Capture  Goals: –Realistic motion –Lots of different motions (300-1000) –Contact  Appropriate game genres –Sports –Fighting –Human characters

35 Oct 16, Fall 2006IAT 41035 Applications Movies, TV Video games Performance animation

36 Oct 16, Fall 2006IAT 41036

37 Oct 16, Fall 2006IAT 41037

38 Oct 16, Fall 2006IAT 41038 Plan out Shoots Carefully  Know needed actions (80-100 takes/day) –Bridges between actions –Speed of actions –Starting/ending positions  Hire the right actor –Watch for idiosyncrasies in motion –Good match in proportions

39 Oct 16, Fall 2006IAT 41039 Sensor Placement  Place markers carefully –Capture enough information –Watch for marker movement  Check data part way through shoot  Videotape everything!

40 Oct 16, Fall 2006IAT 41040

41 Oct 16, Fall 2006IAT 41041 Technology  Numerous technologies  Record energy transfer –Light –Electromagnetism –Mechanical skeletons

42 Oct 16, Fall 2006IAT 41042 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

43 Oct 16, Fall 2006IAT 41043 Technology  Passive reflection --Acclaim, Motion Analysis –Automatically digitized –240Hz –Not real-time, Correspondence –3+ markers/body part –2+ cameras for 3D position data

44 Oct 16, Fall 2006IAT 41044 Technology  Vicon Motion Systems –Retroreflective paint on reflectors –Lights on camera –Very high contrast markers

45 Oct 16, Fall 2006IAT 41045 Technology  Active light sources -- Optotrak –Automatically digitized –256 markers –3500 marker/sec –Real-time –Specialized cameras

46 Oct 16, Fall 2006IAT 41046 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 –30-144 Hz13-18 markers

47 Oct 16, Fall 2006IAT 41047 Technology  Exoskeleton + angle sensors –Analogous –Tethered –No identification problem –Realtime- 500Hz –No range limit- Fit –Rigid body approximation

48 Oct 16, Fall 2006IAT 41048 Technology  Dataglove –Low accuracy –Focused resolution  Monkey –High accuracy –High data rate –Not realistic motion –No paid actor Mechanical motion capture

49 Oct 16, Fall 2006IAT 41049 Technology  Technology issues –Resolution/range of motion –Calibration –Accuracy –Occlusion/Correspondence

50 Oct 16, Fall 2006IAT 41050 Animation Issues  Style  Scaling  Generalization

51 Oct 16, Fall 2006IAT 41051 Resolution  Positioning of camera

52 Oct 16, Fall 2006IAT 41052 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

53 Oct 16, Fall 2006IAT 41053 Calibration  Finding Joint Locations –Move markers to joint centers Assume rigid links, rotary joints  Shoulder?

54 Oct 16, Fall 2006IAT 41054 Calibration  Extract best limb lengths  Use estimator to compute limb length  Minimize or reject outliers

55 Oct 16, Fall 2006IAT 41055 Calibration  Example estimator: –508 frames of walking –6 bad frames –Collarbone to shoulder: Hand editing: 13.3cm Estimator: 13.2cm Arithmetic mean: 14.1cm

56 Oct 16, Fall 2006IAT 41056 Accuracy  Marker movement  Noise in sensor readings  Skew in measurement time  Environment restrictions  Frame rate –High frame rate allows good filtering

57 Oct 16, Fall 2006IAT 41057 Camera Calibration  Internal camera parameters –Optical distortion of lens  External parameters –Position and orientation  Correlation between multiple cameras

58 Oct 16, Fall 2006IAT 41058 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

59 Oct 16, Fall 2006IAT 41059 Scaling Animation  Contact  Movement style  Inverse kinematics

60 Oct 16, Fall 2006IAT 41060 Generalizating Animation  Interpolation Synthesis for Articulated Figure Motion  Wiley and Hahn  IEEE CG&A v17#6

61 Oct 16, Fall 2006IAT 41061 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

62 Oct 16, Fall 2006IAT 41062 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

63 Oct 16, Fall 2006IAT 41063 Generalizating Animation  Motion Editing With Spacetime Constraints –Michael Gleicher –1997 Symposium on Interactive 3D Graphics

64 Oct 16, Fall 2006IAT 41064 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

65 Oct 16, Fall 2006IAT 41065 Simulation  Modeling the real world with simple physics –Realism –A set of rules –Better interactivity  Objects or Characters

66 Oct 16, Fall 2006IAT 41066 Passive -- No muscles or motors Active -- Internal source of energy

67 Oct 16, Fall 2006IAT 41067 Equations of Motion  Water  Explosions  Rigid body models

68 Oct 16, Fall 2006IAT 41068 Control Systems  Wide variety of behaviors  Transitions between behaviors  Controllable by AI or UI  Robust

69 Oct 16, Fall 2006IAT 41069 Equations of Motion

70 Oct 16, Fall 2006IAT 41070 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

71 Oct 16, Fall 2006IAT 41071 Keyframing  Fine level of control  Quality of motion depends on skill of animator

72 Oct 16, Fall 2006IAT 41072 Motion Capture  Natural-looking motion  Hard to generalize motions –Registration is difficult  Often seems “weightless” – Bill Kroyer, Rhythm & Hues

73 Oct 16, Fall 2006IAT 41073 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

74 Oct 16, Fall 2006IAT 41074 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

75 Oct 16, Fall 2006IAT 41075 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


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