Goal-directed, dynamic animation of human walking

Bruderlin, Armin; Calvert, Tom

doi:10.1145/74333.74357

Cited by 142 publications

(44 citation statements)

References 8 publications

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“…Hand-animating characters is time-consuming, even digitally, and the task has long been supplemented (sometimes replaced) by computational models to control movement [170]. Initially, motion controllers [171] for characters emerged from motor control in engineering (and, later, in robotics) that used virtual actuators on virtual joints (which matched to nodes on digital character rigs) [172,173].…”

Section: Animationmentioning

confidence: 99%

“…Other motion control schemes were developed as finite state machines [174], which allowed animators to develop procedural rules for characters [175]. Also, control schemes within origins outside computing have been woven into computing (see Pelechano et al [64] for an excellent overview): physics models are popularly used, as are psychology-like approaches [176,177], cognitive schemes [170,178,179], group traits derived from collective human and animal behavior [180][181][182], machine-learning models that build control functions from trajectory data of real people [183][184][185], and schemes that afford computational efficiency in control across complex solution spaces [186,187]. Of particular relevance is the tradition of using the built environment (urban morphology, road networks, naturalistic paths, and implied movement effort) to impose hierarchies or abstractions that might ease look-up schemes in model databases, balance rendering loads in animation, and scale crowds to large populations [188][189][190][191][192][193][194][195].…”

Section: Animationmentioning

confidence: 99%

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Computational Streetscapes

Torrens

2016

Computation

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Abstract:Streetscapes have presented a long-standing interest in many fields. Recently, there has been a resurgence of attention on streetscape issues, catalyzed in large part by computing. Because of computing, there is more understanding, vistas, data, and analysis of and on streetscape phenomena than ever before. This diversity of lenses trained on streetscapes permits us to address long-standing questions, such as how people use information while mobile, how interactions with people and things occur on streets, how we might safeguard crowds, how we can design services to assist pedestrians, and how we could better support special populations as they traverse cities. Amid each of these avenues of inquiry, computing is facilitating new ways of posing these questions, particularly by expanding the scope of what-if exploration that is possible. With assistance from computing, consideration of streetscapes now reaches across scales, from the neurological interactions that form among place cells in the brain up to informatics that afford real-time views of activity over whole urban spaces. For some streetscape phenomena, computing allows us to build realistic but synthetic facsimiles in computation, which can function as artificial laboratories for testing ideas. In this paper, I review the domain science for studying streetscapes from vantages in physics, urban studies, animation and the visual arts, psychology, biology, and behavioral geography. I also review the computational developments shaping streetscape science, with particular emphasis on modeling and simulation as informed by data acquisition and generation, data models, path-planning heuristics, artificial intelligence for navigation and way-finding, timing, synthetic vision, steering routines, kinematics, and geometrical treatment of collision detection and avoidance. I also discuss the implications that the advances in computing streetscapes might have on emerging developments in cyber-physical systems and new developments in urban computing and mobile computing.

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Section: Animationmentioning

confidence: 99%

Section: Animationmentioning

confidence: 99%

Computational Streetscapes

Torrens

2016

Computation

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“…Girard (Girard, 1987), (Girard, 1991) and Girard & Maciejewski (Girard and Maciejewski, 1985), also used a hybrid kinematics/dynamics algorithm and therefore all the problems associated with the kinematics approach were again present when attempting to control articulated figures. A similar approach has been adopted by Bruderlin and Calvert (Bruderlin and Calvert, 1989) and is discussed by Calvert (Calvert, 1991). Isaacs and Cohen (Isaacs and Cohen, 1987) presented another combination of kinematics and inverse dynamics specifications.…”

Section: Second Generationmentioning

confidence: 99%

Human Figure Animation: A Historical Perspective

Chrysanthou¹,

Loizides²

2008

ECMS 2008 Proceedings Edited By: L. S. Louca, Y. Chrysanthou, Z. Oplatkova, K. Al-Begain

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Computer Animation, has advanced significantly as well as spectacularly over the last two-to-three decades. The interesting field has innovated tremendously moving from first-generation i.e. purely geometric models where motion was developed using kinematic techniques, to secondgeneration i.e. physical-based models (direct versus inverse dynamics and hybrid systems), towards the development of the third-generation i.e. autonomous behaviour through learning and perception. The simulation and animation of a variety of real-world objects appear with stunning realism. However, the main issue here is not on what we have achieved but rather, based on what we have achieved, what is to follow. The emerging results are expected immensely by researchers in the field, worldwide. The paper delivers a survey on human figure animation and in particular a classification framework of the work done on the control of locomotion.

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“…Girard [9] uses a mix of kinematic and dynamic methods to achieve a variety of biped and quadruped motions. Bruderlin and Calvert [5] use a similar mix of techniques to generate realistic parameterized walking motions for a kinematically complex human model, and later show that parameterized walks can also be achieved using a purely kinematic model [6]. The gaits are constructed using important features extracted from experimental gait data.…”

Section: Previous Workmentioning

confidence: 99%

Guided Optimization for Balanced Locomotion

Panne

Lamouret

1995

Eurographics

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Abstract. Teaching simulated creatures how to walk and run is a challenging problem. As with a baby learning to walk, however, the task of synthesizing the necessary muscle control is simplified if an external hand to assist in maintaining balance is provided. A method of using guiding forces to allow progressive learning of control actions for balanced locomotion is presented. The process has three stages. Stage one involves using a "hand of God" to facilitate balance while the basic actions of a desired motion are learned. Stage two reduces the dependence on external guidance, yielding a more balanced motion. Where possible, a third stage removes the external guidance completely to produce a free, balanced motion. The method is applied to obtain walking motions for a simple biped and a bird-like mechanical creature, as well as walking, running, and skipping motions for a human model of realistic proportions.

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Goal-directed, dynamic animation of human walking

Cited by 142 publications

References 8 publications

Computational Streetscapes

Computational Streetscapes

Human Figure Animation: A Historical Perspective

Guided Optimization for Balanced Locomotion

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