Estimating contact dynamics

Brubaker, Marcus A.; Sigal, Leonid; Fleet, David J.

doi:10.1109/iccv.2009.5459407

Cited by 95 publications

(70 citation statements)

References 24 publications

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“…This has been typically used in computer graphics for simulation purposes (Baraff 1989;Popovic and Witkin 1999), and further exported to computer vision applications, for non-rigid tracking of surfaces (Metaxas and Terzopoulos 1993) or articulated bodies (Brubaker et al 2009;Salzmann and Urtasun 2011;Vondrak et al 2008). Yet, none of these approaches 4 A.…”

Section: Related Workmentioning

confidence: 99%

“…Drawing inspiration from computer graphics (Popovic and Witkin 1999), there have been several attempts at using these models for tracking non-rigid motion (Metaxas and Terzopoulos 1993) and modeling human activities (Brubaker et al 2009). Unfortunately, these methods are usually focused to specific types of motion, and their underlying laws rely on non-linear relations complex to optimize.…”

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confidence: 99%

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Combining Local-Physical and Global-Statistical Models for Sequential Deformable Shape from Motion

Agudo

Moreno-Noguer

2016

Int J Comput Vis

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In this paper, we simultaneously estimate camera pose and non-rigid 3D shape from a monocular video, using a sequential solution that combines local and global representations. We model the object as an ensemble of particles, each ruled by the linear equation of the Newton's second law of motion. This dynamic model is incorporated into a bundle adjustment framework, in combination with simple regularization components that ensure temporal and spatial consistency. The resulting approach allows to sequentially estimate shape and camera poses, while progressively learning a global low-rank model of the shape that is fed back into the optimization scheme, introducing thus, global constraints. The overall combination of local (physical) and global (statistical) constraints yields a solution that is both efficient and robust to several artifacts such as noisy and missing data or sudden camera motions, without requiring any training data at all. Validation is done in a variety of real application domains, including articulated and non-rigid motion, both for continuous and discontinuous shapes. Our on-line methodology yields significantly more accurate reconstructions than competing sequential approaches, being even comparable to the more computationally demanding batch methods.

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Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

Combining Local-Physical and Global-Statistical Models for Sequential Deformable Shape from Motion

Agudo

Moreno-Noguer

2016

Int J Comput Vis

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“…The values of all the mechanical parameters used in the model are shown in Table 1, where M is the total mass of the robot. The model assumes that the ground is adequately soft, i.e., the contact points can be placed "below" the ground, and there is no pulling force exerting on G i when the contact points are above the ground [27,28]. Here, f g is the gravitational force, k x , k y and k z are the spring coefficients in the x, y and z direction respectively, while d x , d y and d z are damper coefficients.…”

Section: Model and Dynamics Of A Curved Beam Hopping Robotmentioning

confidence: 99%

Guided Self-Organization in a Dynamic Embodied System Based on Attractor Selection Mechanism

Nurzaman

Kim

et al. 2014

Entropy

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Guided self-organization can be regarded as a paradigm proposed to understand how to guide a self-organizing system towards desirable behaviors, while maintaining its non-deterministic dynamics with emergent features. It is, however, not a trivial problem to guide the self-organizing behavior of physically embodied systems like robots, as the behavioral dynamics are results of interactions among their controller, mechanical dynamics of the body, and the environment. This paper presents a guided self-organization approach for dynamic robots based on a coupling between the system mechanical dynamics with an internal control structure known as the attractor selection mechanism. The mechanism enables the robot to gracefully shift between random and deterministic behaviors, represented by a number of attractors, depending on internally generated stochastic perturbation and sensory input. The robot used in this paper is a simulated curved beam hopping robot: a system with a variety of mechanical dynamics which depends on its actuation frequencies. Despite the simplicity of the approach, it will be shown how the approach regulates the probability of the robot to reach a goal through the interplay among the sensory input, the level of inherent stochastic perturbation, i.e., noise, and the mechanical dynamics.

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“…Brubaker et al [6,7,9] employed realistic metaphors of the lower body dynamics to estimate and predict walking. Going further, they incorporated a friction model for a ground that affords human motion upon it [8].…”

Section: Relevant Workmentioning

confidence: 99%

Binding Computer Vision to Physics Based Simulation: The Case Study of a Bouncing Ball

Kyriazis¹,

Oikonomidis²,

Argyros³

2011

Procedings of the British Machine Vision Conference 2011

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A dynamic scene and, therefore, its visual observations are invariably determined by the laws of physics. We demonstrate an illustrative case where physical explanation, as a vision prior, is not a commodity but a necessity. By considering the problem of ball motion estimation we show how physics-based simulation in conjunction with visual processes can lead to the reduction of the visual input required to infer physical attributes of the observed world. Even further, we show that the proposed methodology manages to reveal certain physical attributes of the observed scene that are difficult or even impossible to extract by other means. A series of experiments on synthetic data as well as experiments with image sequences of an actual ball, support the validity of the proposed approach. The use of generic tools and the top-down nature of the proposed approach make it general enough to be a likely candidate for handling even more complex problems in larger contexts.

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Estimating contact dynamics

Abstract: Abstract

Cited by 95 publications

References 24 publications

Combining Local-Physical and Global-Statistical Models for Sequential Deformable Shape from Motion

Combining Local-Physical and Global-Statistical Models for Sequential Deformable Shape from Motion

Guided Self-Organization in a Dynamic Embodied System Based on Attractor Selection Mechanism

Binding Computer Vision to Physics Based Simulation: The Case Study of a Bouncing Ball

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