UNC-Chapel Hill COMP 259 - LECTURE NOTES (18 pages)

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LECTURE NOTES



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LECTURE NOTES

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Pages:
18
School:
University of North Carolina at Chapel Hill
Course:
Comp 259 - Physically-Based Modeling, Simulation and Animation
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Reading Assignments Principles of Traditional Animation Applied to 3D Computer Animation by J Lasseter Proc of ACM SIGGRAPH 1987 Computer Animation Algorithms and Techniques by Richard Parent 2001 Chapter 1 4 5 and Appendices Advanced Animation and Rendering Techniques Theory and Practice by A Watt and M Watt 1992 Chapter 15 16 UNC Chapel Hill M C Lin Basics of Motion Generation let Xi configuration of Oi at tk t0 i END false while not END do display Oi i tk tk t generate Xi at tk i END function motion generation UNC Chapel Hill M C Lin Methods of Motion Generation Traditional Principles Keyframing Performance Capture Motion Capture Modeling Simulation Automatic UNC Chapel Hill Physics Behaviors Discovery High Level Control M C Lin Applications Choices Computer Animation Virtual Environments Rapid Prototyping Haptic Rendering Computer Game Dynamics Robotics and Automation Medical Simulation and Analysis UNC Chapel Hill M C Lin Keyframing I 1 Specify the key positions for the objects to be animated 2 Interpolate to determines the position of in between frames UNC Chapel Hill M C Lin Keyframing II Advantages Relatively easy to use Providing low level control Problems Tedious and slow Requiring the animator to understand the intimate details about the animated objects and the creativity to express their behavior in key frames UNC Chapel Hill M C Lin Motion Interpolation Interpolate using mathematical functions Linear Hermite Bezier and many others see Appendices of Richard Parent s online book Forward inverse kinematics for articulation Specifying UNC Chapel Hill representing deformation M C Lin Basic Terminologies Kinematics study of motion independent of underlying forces Degrees of freedom DoF the number of independent position variables needed to specify motions State Vector vector space of all possible configurations of an articulated figure In general the dimensions of state vector is equal to the DoF of the articulated figure UNC Chapel Hill M C Lin Forward vs Inverse Kinematics Forward kinematics motion of all joints is explicitly specified Inverse kinematics given the position of the end effector find the position and orientation of all joints in a hierarchy of linkages also called goal directed motion See an in class example UNC Chapel Hill M C Lin Forward Kinematics As DoF increases there are more transformation to control and thus become more complicated to control the motion Motion capture can simplify the process for well defined motions and pre determined tasks UNC Chapel Hill M C Lin Inverse Kinematics As DoF increases the solution to the problem may become undefined and the system is said to be redundant By adding more constraints reduces the dimensions of the solution It s simple to use when it works But it gives less control Some common problems Existence of solutions Multiple solutions Methods used UNC Chapel Hill M C Lin Modeling Deformation Geometric based Techniques Global local deformation Barr 84 FFD Sederberg Parry 86 and variants others Physically based Techniques particle systems BEM FEM FEA Variational Techniques Variational surface modeling Welch Witkin 92 dynamic NURBS Terzopoulos Qin 94 UNC Chapel Hill M C Lin Motion Capture I 1 Use special sensors trackers to record the motion of a performer 2 Recorded data is then used to generate motion for an animated character figure UNC Chapel Hill M C Lin Motion Capture II Advantages Ease of generating realistic motions Problems Not easy to accurately measure motions Difficult to scale or adjust the recorded motions to fit the size of the animated characters Limited capturing technology devices Sensor noise due to magnetic metal trackers Restricted motion due to wires cables Limited working volume UNC Chapel Hill M C Lin Physically based Simulation I Use the laws of physics or a good approximation to generate motions Primary Active vs secondary actions vs passive systems Dynamic UNC Chapel Hill vs static simulation M C Lin Physically based Simulation II Advantages Relatively easy to generate a family of similar motions Can be used for describing realistic complex animation e g deformation Can generate reproducible motions Problems Challenging to build a simulator as it requires in depth understanding of physics mathematics Less low level control by the user UNC Chapel Hill M C Lin High Level Control I Task level description using AI techniques Collision avoidance Motion planning Rule based reasoning Genetic algorithms etc UNC Chapel Hill M C Lin High Level Control II Advantages Very easy to specify generate motions Can reproduce realistic motions Problems Need to specify all possible rules The intelligence of the system is limited by its input or training May not be reusable across different applications domains UNC Chapel Hill M C Lin


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