Physical simulation for probabilistic motion tracking

Vondrak, Marek; Sigal, Leonid; Jenkins, Odest Chadwicke

doi:10.1109/cvpr.2008.4587580

Cited by 95 publications

(68 citation statements)

References 20 publications

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“…There will, generally, be a trade-off between model generality and the difficulty of designing a controller (Vondrak et al 2008). We note that, while control of humanoid dynamical models is a challenging problem, there is a substantial literature in robotics and animation from which to draw inspiration.…”

Section: Discussion and Future Workmentioning

confidence: 99%

Physics-Based Person Tracking Using the Anthropomorphic Walker

2009

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We introduce a physics-based model for 3D person tracking. Based on a biomechanical characterization of lower-body dynamics, the model captures important physical properties of bipedal locomotion such as balance and ground contact. The model generalizes naturally to variations in style due to changes in speed, step-length, and mass, and avoids common problems (such as footskate) that arise with existing trackers. The dynamics comprise a two degreeof-freedom representation of human locomotion with inelastic ground contact. A stochastic controller generates impulsive forces during the toe-off stage of walking, and springlike forces between the legs. A higher-dimensional kinematic body model is conditioned on the underlying dynamics. The combined model is used to track walking people in video, including examples with turning, occlusion, and varying gait. We also report quantitative monocular and binocular tracking results with the HumanEva dataset.

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Section: Discussion and Future Workmentioning

confidence: 99%

Physics-Based Person Tracking Using the Anthropomorphic Walker

2009

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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%

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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“…Popović and Witkin [21] rectified 3D motion capture data to make them compliant to physical constraints. Vondrak et al [24] fused motion planning, contact dynamics and a ground assumption to track humans from multiple cameras. Brubaker et al [6,7,9] employed realistic metaphors of the lower body dynamics to estimate and predict walking.…”

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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Physical simulation for probabilistic motion tracking

Abstract: Human motion tracking is an important problem in com-

Cited by 95 publications

References 20 publications

Physics-Based Person Tracking Using the Anthropomorphic Walker

Physics-Based Person Tracking Using the Anthropomorphic Walker

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

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

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