Sampling plausible solutions to multi-body constraint problems

Chenney, Stephen; Forsyth, David

doi:10.1145/344779.344882

Cited by 94 publications

(69 citation statements)

References 30 publications

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“…Sampling-based approach for controlling the outcome of physical simulation has been applied to rigid body [Chenney and Forsyth 2000;Twigg and James 2007] and various character animatioins [van de Panne and Fiume 1993;Ngo and Marks 1993;Sims 1994;Liu et al 2010;Sok et al 2007]. For methods involving actively controlled systems, determining a proper sampling space is crucial.…”

Section: Related Workmentioning

confidence: 99%

Synthesis of detailed hand manipulations using contact sampling

Liu

2012

ACM Trans. Graph.

112

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Capturing human activities that involve both gross full-body motion and detailed hand manipulation of objects is challenging for standard motion capture systems. We introduce a new method for creating natural scenes with such human activities. The input to our method includes motions of the full-body and the objects acquired simultaneously by a standard motion capture system. Our method then automatically synthesizes detailed and physically plausible hand manipulation that can seamlessly integrate with the input motions. Instead of producing one "optimal" solution, our method presents a set of motions that exploit a wide variety of manipulation strategies. We propose a randomized sampling algorithm to search for as many as possible visually diverse solutions within the computational time budget. Our results highlight complex strategies human hands employ effortlessly and unconsciously, such as static, sliding, rolling, as well as finger gaits with discrete relocation of contact points.

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Section: Related Workmentioning

confidence: 99%

Synthesis of detailed hand manipulations using contact sampling

Liu

2012

ACM Trans. Graph.

112

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“…It is worth noting that Chenney and Forsyth [6] have shown that samples such as those generated by our probabilistic tree search can be used to generate motions satisfying various external constraints.…”

Section: Related Workmentioning

confidence: 99%

“…Finally, it is important to note that the motion model has no notion of gravity, friction, and position of the feet relative to the floor. To be able to generate plausible-looking motions for longer periods, constraints need to be introduced on the position and rotation of the human model so that it fulfills basic kinematic and dynamic requirements [6,11]. Furthermore, in general it is desired to be able to edit the generated motion iteratively, and to introduce goals in the motion generation [26].…”

Section: Visualizing the Model (Synthesis)mentioning

confidence: 99%

Implicit Probabilistic Models of Human Motion for Synthesis and Tracking

Sidenbladh

Black

Sigal

2002

Lecture Notes in Computer Science

231

188

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Abstract. This paper addresses the problem of probabilistically modeling 3D human motion for synthesis and tracking. Given the high dimensional nature of human motion, learning an explicit probabilistic model from available training data is currently impractical. Instead we exploit methods from texture synthesis that treat images as representing an implicit empirical distribution. These methods replace the problem of representing the probability of a texture pattern with that of searching the training data for similar instances of that pattern. We extend this idea to temporal data representing 3D human motion with a large database of example motions. To make the method useful in practice, we must address the problem of efficient search in a large training set; efficiency is particularly important for tracking. Towards that end, we learn a low dimensional linear model of human motion that is used to structure the example motion database into a binary tree. An approximate probabilistic tree search method exploits the coefficients of this low-dimensional representation and runs in sub-linear time. This probabilistic tree search returns a particular sample human motion with probability approximating the true distribution of human motions in the database. This sampling method is suitable for use with particle filtering techniques and is applied to articulated 3D tracking of humans within a Bayesian framework. Successful tracking results are presented, along with examples of synthesizing human motion using the model.

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“…In (Chenney and Forsyth, 1997), objects outside the current view are no longer updated based on physical laws. In (Carlson and Hodgins, 1997), the motions of legged creatures not in view are updated based on simplified rules, while (Barzel et al, 1996;Chenney and Forsyth, 2000) maintain that plausibility as opposed to accuracy is acceptable in many situations, and examine ways in which physically plausible simulations may be generated. However, perceptual principles have to date been largely neglected when considering the issue of level of detail (LOD) for simulation.…”

Section: Simulationmentioning

confidence: 99%

Eye-Movements and Interactive Graphics

O’Sullivan¹,

Dingliana²,

Howlett³

2003

The Mind's Eye

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In this chapter, we will discuss the usefulness of eye-tracking for computer applications that attempt to render simulations at interactive rates. Some recent research that uses this technology is presented. While eyetracking has been relatively well-explored for the purposes of accelerating real-time rendering, it has not been very much considered with respect to the actual simulation process. In fact, perceptual issues relating to physically-based simulation have been largely neglected in the field of computer graphics. To evaluate simulations, we explore the relationship between eye-movements and dynamic events, such as collisions. We will describe some gaze-contingent systems that we and others have developed that exploit eye-tracking to enhance the perceived realism of images and simulations. Some experiments that were carried out to evaluate the feasibility of this approach are also presented.

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Sampling plausible solutions to multi-body constraint problems

Cited by 94 publications

References 30 publications

Synthesis of detailed hand manipulations using contact sampling

Synthesis of detailed hand manipulations using contact sampling

Implicit Probabilistic Models of Human Motion for Synthesis and Tracking

Eye-Movements and Interactive Graphics

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