State-of-the-Art in Computer Animation
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State-of-the-Art in Computer Animation
Nadia Magnenat Thalmann MIRALab, University of Geneva Daniel Thalmann Computer Graphics Lab, EPFL Computer animation may be defined as a technique in which the illusion of movement is created by displaying on a screen, or recording on a device a series of individual states of a dynamic scene. The key issue of Computer Animation is the way of defining motion, what is commonly known as Motion Control Methods (MCMs). MCMs may be classified [2] based on the nature of the information, which is directly manipulated: geometric, physical, or behavioral. Methods based on Geometric and Kinematics information These methods are heavily relied upon the animator. Motion is locally controlled and defined in terms of coordinates, angles, velocities, or accelerations. The simplest approach is Performance Animation which consists in magnetic or optical measurement and recording of direct actions of a real person for immediate or delayed playback. The technique is especially used today in production environments for 3D character animation. Key frame animation is still another popular technique in which the animator explicitly specifies the kinematics by supplying keyframes values whose "in-between" frames are interpolated by the computer. Inverse kinematics[3] is a technique coming from robotics, where the motion of links of a chain is computed from the end link trajectory. Image Morphing[4] 5 is a warping-based technique which interpolates the features between two images to obtain a natural inbetween image. For geometric deformations, multi-layered models [6] 7 8 are particularly useful for modelling 3D characters. We should also mention the specific research in facial animation [9] 10 11 12. Methods based on Physical information In these methods, the animator provides physical data and the motion is obtained by solving the dynamic equations. Motion is globally controlled. We may distinguish methods based on parameter adjustment 13 and constraint-based methods, where the animator states in terms of constraints the properties the model is supposed to have, without needing to adjust parameters. For example, Witkin and Kass[14] propose the concept of Spacetime Constraints, for creating character animation by solving constrained optimization. Cohen[15] takes this concept further and uses spacetime window to control the animation interactively. For realistic simulation of deformations, Terzopoulos et al. use an elastic model[16] based on the Lagrange equation. Gourret et al.[17] propose a finite element method to model the deformations of human flesh due to flexion of members and contact with objects. Deformation of
animated clothes [18] 19 is also an active area of physics-based animation. In physics-based animation, collision detection and response are obviously important. Hahn[20] prevents bodies in resting contact as a series of frequently occurring collisions. Baraff[21] presents an analytical method for finding forces between contacting polyhedral bodies. Methods based on Behavioral information A behavioral motion control method consists of driving the behavior of autonomous creatures by providing high-level directives indicating a specific behavior without any other stimulus. Reynolds[22] introduces a distributed behavioral model to simulate flocks of birds and schools of fish. Wilhelms[23] proposes a system based on a network of sensors and effectors. Tu and Terzopoulos[24] describe a world inhabited by autonomous artificial fishes. In order to implement perception, virtual creatures should be equipped with virtual sensors, used as a basis for implementing behaviour such as visually directed locomotion [25] 26 27 or handling objects[28] 29. The concept of synthetic vision was first introduced by Renault et al. 30 as a main information channel between the environment and a virtual actor. Reynolds[31] describes an evolved, vision-based behavioral model of coordinated group motion. Noser et al.[32] propose the use of an octree as the internal representation of the environment seen by an actor. Conclusion Computer animation tends to be more and more based on dynamic simulation methods and behavioral animation. With the existence of super graphics workstations and the advent of VR devices, it is possible to create applications based on a full 3-D interaction metaphor in which the specifications of deformations or motion are given in real-time. This new concepts drastically change the way of designing animation sequences. In the future, an integration between simulation methods, artificial life, and VR-based animation will lead to systems allowing the user to interact with complex virtual worlds with living creatures. [1] Published in ACM Computing Surveys, 1996 [2] N.Magnenat Thalmann N, D.Thalmann (1991) Complex Models for Animating Synthetic Actors, IEEE CG&A, 11(5)32-44. [3] N.I Badler, J.D.Korein, J.U.Korein, G.M.Radack, L.S.Brotman (1985) Positioning and Animating Figures in a Task-oriented Environment, The Visual Computer, 1(4)212-220 [4] G. Wolberg (1990) Digital Image Warping, IEEE Computer Society Press.] [5] S.Lee, S.Y.Shin (1995) Warp Generation and Transition Control in Image Morphing in: N.Magnenat Thalmann, D.Thalmann (eds) Interactive Computer Animation, Prentice Hall. [6] J.E.Chadwick, D.R.Haumann, R.E.Parent (1989) Layered construction for deformable animated character, Proc.SIGGRAPH'89, pp.243-252] [7] M.P.Gascuel, A.Verroust, C.Puech (1991) A modelling system for complex deformable bodies suited to animation and collision processing, Journal of Visualization and Computer Animation, 2, pp.82-90. [8] S. Jianhua, D.Thalmann (1995) Interactive shape design using metaballs and splines,
Proc.Implicit Surfaces`95, Grenoble. [9] P.Kalra, A.Mangili, N.Magnenat-Thalmann, D.Thalmann (1992) Simulation of Facial Muscle Actions Based on Rational Free Form Deformations, Proc.Eurographics'92. [10] K.Waters (1987) A Muscle Model for Animating Three-Dimensional Facial Expression, Proc.SIGGRAPH '87, pp.17-24. [11] L.Williams (1990) Performance Driven Facial Animation, Proc SIGGRAPH '90, pp. 235242. [12] K.Waters, D.Terzopoulos (1991) Modeling and Animating Faces using Scanned Data, Journal of Visualization and Computer Animation, 2(4)123-128. [13] W.W.Armstrong, M.Green, R.Lake (1987) Near real-time Control of Human Figure Models, IEEE CG&A, 7( 6)28-38. [14] A.Witkin, M.Kass (1988) Spacetime Constraints, Proc.SIGGRAPH '88, pp.159-168. [15] M.F.Cohen (1992) Interactive Spacetime Control for Animation, Proc. Siggraph'92, pp.293302. [16] D.Terzopoulos, J.C.Platt, A.H.Barr, K.Fleischer (1987) Elastically Deformable Models, Proc.SIGGRAPH'87, pp.205-214. [17] J.P.Gourret, N.Magnenat-Thalmann, D.Thalmann (1989) Simulation of Object and Human Skin Deformations in a Grasping Task, Proc.SIGGRAPH '89, pp.21-30. [18] M.Carignan, Y.Yang, N.Magnenat Thalmann, D.Thalmann (1992) Dressing Animated Synthetic Actors with Complex Deformable Clothes, Proc.SIGGRAPH '92, pp.99-104. [19] M.Courchesnes, P.Volino, N.Magnenat Thalmann (1995) Versatile and Efficient Techniques for Simulating Cloth and Other Deformable Objects, Proc. SIGGRAPH `95. [20] J.K.Hahn (1988) Realistic Animation of Rigid Bodies, Proc.SIGGRAPH'88, pp.299-308. [21] D.Baraff (1989) Analytical Methods for Dynamic Simulation of Non-Penetrating Rigid Bodies, Proc. SIGGRAPH '89, pp.223-232. [22] C.Reynolds (1987) Flocks, Herds, and Schools: A Distributed Behavioral Model, Proc.SIGGRAPH '87, pp.25-34. [23] J.Wilhelms (1990) A Notion for Interactive Behavioral Animation Control, IEEE CG&A, 10(3)14-22 [24] X.Tu, D.Terzopoulos (1994) Artificial Fishes: Physics, Locomotion, Perception, Behavior, Proc.SIGGRAPH'94, pp.42-48. [25] D. Zeltzer (1982) Motor Control Techniques for Figure Animation, IEEE CG&A , 2(9)5359.
[26] A.Bruderlin, T.W.Calvert (1989) Goal Directed, Dynamic Animation of Human Walking, Proc.SIGGRAPH'89, pp.233-242 [27] R. Boulic, D. Thalmann, N. Magnenat-Thalmann (1990) A Global Human Walking Model with Real Time Kinematic Personification, The Visual Computer, 6(6). [28] H.Rijpkema, M.Girard (1991) Computer Animation of Knowledge-based Grasping, Proc.SIGGRAPH`91, pp.339-348.] [29] R.Mas, D.Thalmann (1994) A Hand Control and Automatic Grasping System for Synthetic Actors, Proc.Eurographic'94, pp.167-178. [30] O.Renault, N.Magnenat-Thalmann, D.Thalmann (1990) A Vision-Based Approach to Behavioural Animation, Visualization and Computer Animation Journal,1(1). [31] C.W.Reynolds (1993) An Evolved, Vision-Based Behavioral Model of Coordinated Group Motion, in: J.A.Meyer et al. (eds) From Animals to Animats, Proc. 2nd International Conf. on Simulation of Adaptive Behavior, MIT Press, 1993, pp.384-392. [32] H.Noser, O.Renault, D.Thalmann, N.Magnenat Thalmann (1995) Navigation for Digital Actors based on Synthetic Vision, Memory and Learning, Computers and Graphics, 19(1)7-19.
State-of-the-Art in Computer Animation
Nadia Magnenat Thalmann MIRALab, University of Geneva Daniel Thalmann Computer Graphics Lab, EPFL Computer animation may be defined as a technique in which the illusion of movement is created by displaying on a screen, or recording on a device a series of individual states of a dynamic scene. The key issue of Computer Animation is the way of defining motion, what is commonly known as Motion Control Methods (MCMs). MCMs may be classified [2] based on the nature of the information, which is directly manipulated: geometric, physical, or behavioral. Methods based on Geometric and Kinematics information These methods are heavily relied upon the animator. Motion is locally controlled and defined in terms of coordinates, angles, velocities, or accelerations. The simplest approach is Performance Animation which consists in magnetic or optical measurement and recording of direct actions of a real person for immediate or delayed playback. The technique is especially used today in production environments for 3D character animation. Key frame animation is still another popular technique in which the animator explicitly specifies the kinematics by supplying keyframes values whose "in-between" frames are interpolated by the computer. Inverse kinematics[3] is a technique coming from robotics, where the motion of links of a chain is computed from the end link trajectory. Image Morphing[4] 5 is a warping-based technique which interpolates the features between two images to obtain a natural inbetween image. For geometric deformations, multi-layered models [6] 7 8 are particularly useful for modelling 3D characters. We should also mention the specific research in facial animation [9] 10 11 12. Methods based on Physical information In these methods, the animator provides physical data and the motion is obtained by solving the dynamic equations. Motion is globally controlled. We may distinguish methods based on parameter adjustment 13 and constraint-based methods, where the animator states in terms of constraints the properties the model is supposed to have, without needing to adjust parameters. For example, Witkin and Kass[14] propose the concept of Spacetime Constraints, for creating character animation by solving constrained optimization. Cohen[15] takes this concept further and uses spacetime window to control the animation interactively. For realistic simulation of deformations, Terzopoulos et al. use an elastic model[16] based on the Lagrange equation. Gourret et al.[17] propose a finite element method to model the deformations of human flesh due to flexion of members and contact with objects. Deformation of
animated clothes [18] 19 is also an active area of physics-based animation. In physics-based animation, collision detection and response are obviously important. Hahn[20] prevents bodies in resting contact as a series of frequently occurring collisions. Baraff[21] presents an analytical method for finding forces between contacting polyhedral bodies. Methods based on Behavioral information A behavioral motion control method consists of driving the behavior of autonomous creatures by providing high-level directives indicating a specific behavior without any other stimulus. Reynolds[22] introduces a distributed behavioral model to simulate flocks of birds and schools of fish. Wilhelms[23] proposes a system based on a network of sensors and effectors. Tu and Terzopoulos[24] describe a world inhabited by autonomous artificial fishes. In order to implement perception, virtual creatures should be equipped with virtual sensors, used as a basis for implementing behaviour such as visually directed locomotion [25] 26 27 or handling objects[28] 29. The concept of synthetic vision was first introduced by Renault et al. 30 as a main information channel between the environment and a virtual actor. Reynolds[31] describes an evolved, vision-based behavioral model of coordinated group motion. Noser et al.[32] propose the use of an octree as the internal representation of the environment seen by an actor. Conclusion Computer animation tends to be more and more based on dynamic simulation methods and behavioral animation. With the existence of super graphics workstations and the advent of VR devices, it is possible to create applications based on a full 3-D interaction metaphor in which the specifications of deformations or motion are given in real-time. This new concepts drastically change the way of designing animation sequences. In the future, an integration between simulation methods, artificial life, and VR-based animation will lead to systems allowing the user to interact with complex virtual worlds with living creatures. [1] Published in ACM Computing Surveys, 1996 [2] N.Magnenat Thalmann N, D.Thalmann (1991) Complex Models for Animating Synthetic Actors, IEEE CG&A, 11(5)32-44. [3] N.I Badler, J.D.Korein, J.U.Korein, G.M.Radack, L.S.Brotman (1985) Positioning and Animating Figures in a Task-oriented Environment, The Visual Computer, 1(4)212-220 [4] G. Wolberg (1990) Digital Image Warping, IEEE Computer Society Press.] [5] S.Lee, S.Y.Shin (1995) Warp Generation and Transition Control in Image Morphing in: N.Magnenat Thalmann, D.Thalmann (eds) Interactive Computer Animation, Prentice Hall. [6] J.E.Chadwick, D.R.Haumann, R.E.Parent (1989) Layered construction for deformable animated character, Proc.SIGGRAPH'89, pp.243-252] [7] M.P.Gascuel, A.Verroust, C.Puech (1991) A modelling system for complex deformable bodies suited to animation and collision processing, Journal of Visualization and Computer Animation, 2, pp.82-90. [8] S. Jianhua, D.Thalmann (1995) Interactive shape design using metaballs and splines,
Proc.Implicit Surfaces`95, Grenoble. [9] P.Kalra, A.Mangili, N.Magnenat-Thalmann, D.Thalmann (1992) Simulation of Facial Muscle Actions Based on Rational Free Form Deformations, Proc.Eurographics'92. [10] K.Waters (1987) A Muscle Model for Animating Three-Dimensional Facial Expression, Proc.SIGGRAPH '87, pp.17-24. [11] L.Williams (1990) Performance Driven Facial Animation, Proc SIGGRAPH '90, pp. 235242. [12] K.Waters, D.Terzopoulos (1991) Modeling and Animating Faces using Scanned Data, Journal of Visualization and Computer Animation, 2(4)123-128. [13] W.W.Armstrong, M.Green, R.Lake (1987) Near real-time Control of Human Figure Models, IEEE CG&A, 7( 6)28-38. [14] A.Witkin, M.Kass (1988) Spacetime Constraints, Proc.SIGGRAPH '88, pp.159-168. [15] M.F.Cohen (1992) Interactive Spacetime Control for Animation, Proc. Siggraph'92, pp.293302. [16] D.Terzopoulos, J.C.Platt, A.H.Barr, K.Fleischer (1987) Elastically Deformable Models, Proc.SIGGRAPH'87, pp.205-214. [17] J.P.Gourret, N.Magnenat-Thalmann, D.Thalmann (1989) Simulation of Object and Human Skin Deformations in a Grasping Task, Proc.SIGGRAPH '89, pp.21-30. [18] M.Carignan, Y.Yang, N.Magnenat Thalmann, D.Thalmann (1992) Dressing Animated Synthetic Actors with Complex Deformable Clothes, Proc.SIGGRAPH '92, pp.99-104. [19] M.Courchesnes, P.Volino, N.Magnenat Thalmann (1995) Versatile and Efficient Techniques for Simulating Cloth and Other Deformable Objects, Proc. SIGGRAPH `95. [20] J.K.Hahn (1988) Realistic Animation of Rigid Bodies, Proc.SIGGRAPH'88, pp.299-308. [21] D.Baraff (1989) Analytical Methods for Dynamic Simulation of Non-Penetrating Rigid Bodies, Proc. SIGGRAPH '89, pp.223-232. [22] C.Reynolds (1987) Flocks, Herds, and Schools: A Distributed Behavioral Model, Proc.SIGGRAPH '87, pp.25-34. [23] J.Wilhelms (1990) A Notion for Interactive Behavioral Animation Control, IEEE CG&A, 10(3)14-22 [24] X.Tu, D.Terzopoulos (1994) Artificial Fishes: Physics, Locomotion, Perception, Behavior, Proc.SIGGRAPH'94, pp.42-48. [25] D. Zeltzer (1982) Motor Control Techniques for Figure Animation, IEEE CG&A , 2(9)5359.
[26] A.Bruderlin, T.W.Calvert (1989) Goal Directed, Dynamic Animation of Human Walking, Proc.SIGGRAPH'89, pp.233-242 [27] R. Boulic, D. Thalmann, N. Magnenat-Thalmann (1990) A Global Human Walking Model with Real Time Kinematic Personification, The Visual Computer, 6(6). [28] H.Rijpkema, M.Girard (1991) Computer Animation of Knowledge-based Grasping, Proc.SIGGRAPH`91, pp.339-348.] [29] R.Mas, D.Thalmann (1994) A Hand Control and Automatic Grasping System for Synthetic Actors, Proc.Eurographic'94, pp.167-178. [30] O.Renault, N.Magnenat-Thalmann, D.Thalmann (1990) A Vision-Based Approach to Behavioural Animation, Visualization and Computer Animation Journal,1(1). [31] C.W.Reynolds (1993) An Evolved, Vision-Based Behavioral Model of Coordinated Group Motion, in: J.A.Meyer et al. (eds) From Animals to Animats, Proc. 2nd International Conf. on Simulation of Adaptive Behavior, MIT Press, 1993, pp.384-392. [32] H.Noser, O.Renault, D.Thalmann, N.Magnenat Thalmann (1995) Navigation for Digital Actors based on Synthetic Vision, Memory and Learning, Computers and Graphics, 19(1)7-19.
