Real-time particle filtering with heuristics for 3D motion capture by monocular vision

Jáuregui, David Antonio Gómez; Horain, Patrick; Rajagopal, Manoj Kumar; Karri, Senanayak Sesh Kumar

doi:10.1109/mmsp.2010.5662008

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…We used motion data captured by computer vision from two video lectures (hereafter named V1 and V2) using software developed by Gómez Jáuregui et al, [2] and it outputs the upper body joint angles. 3D wrist positions are obtained from upper body joint angles using forward kinematics [3].…”

Section: Methodsmentioning

confidence: 99%

Animating a Conversational Agent with User Expressivity

Rajagopal

Horain

Pélachaud

2011

Intelligent Virtual Agents

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Abstract. Our objective is to animate an embodied conversational agent (ECA) with communicative gestures rendered with the expressivity of a real human user it represents. We describe an approach to estimate a subset of expressivity parameters defined in the literature (namely spatial and temporal extent) from captured motion trajectories. We first validate this estimation against synthesis motion and then show results with real human motion. The estimated expressivity is then sent to the animation engine of an ECA that becomes a personalized autonomous representative of that user.

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Section: Methodsmentioning

confidence: 99%

Animating a Conversational Agent with User Expressivity

Rajagopal

Horain

Pélachaud

2011

Intelligent Virtual Agents

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“…This search can be sped up through the addition of kinematic reasoning to assist in the sampling, reducing the number of possible solutions to a pose if the lengths of limbs are known [Sminchisescu and Triggs, 2003]. Jauregui et al [2010] also apply kinematic reasoning to aid in pose estimation, but use a silhouette-based observation model. Here, silhouettes are extracted using background subtraction, faces detected and a skin colour model learned.…”

Section: Background and Related Workmentioning

confidence: 99%

Single camera pose estimation using Bayesian filtering and Kinect motion priors

Burke,

Lasenby

2014

Preprint

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Traditional approaches to upper body pose estimation using monocular vision rely on complex body models and a large variety of geometric constraints. We argue that this is not ideal and somewhat inelegant as it results in large processing burdens, and instead attempt to incorporate these constraints through priors obtained directly from training data. A prior distribution covering the probability of a human pose occurring is used to incorporate likely human poses. This distribution is obtained offline, by fitting a Gaussian mixture model to a large dataset of recorded human body poses, tracked using a Kinect sensor. We combine this prior information with a random walk transition model to obtain an upper body model, suitable for use within a recursive Bayesian filtering framework. Our model can be viewed as a mixture of discrete Ornstein-Uhlenbeck processes, in that states behave as random walks, but drift towards a set of typically observed poses. This model is combined with measurements of the human head and hand positions, using recursive Bayesian estimation to incorporate temporal information. Measurements are obtained using face detection and a simple skin colour hand detector, trained using the detected face. The suggested model is designed with analytical tractability in mind and we show that the pose tracking can be Rao-Blackwellised using the mixture Kalman filter, allowing for computational efficiency while still incorporating bio-mechanical properties of the upper body. In addition, the use of the proposed upper body model allows reliable three-dimensional pose estimates to be obtained indirectly for a number of joints that are often difficult to detect using traditional object recognition strategies. Comparisons with Kinect sensor results and the state of the art in 2D pose estimation highlight the efficacy of the proposed approach. 1

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“…using a consumer system such as newly available Kinect [1]. Alternatively, we used motion data captured by computer vision from two video lectures (hereafter named V1 and V2 , see Fig.4 and Fig.5) using software developed by Gómez Jáuregui et al [14] that works by registering a 3D articulated model of the human body onto videos and outputs the upper body joints angles. 3D wrist positions are obtained from upper body joint angles through forward kinematics [7].…”

Section: Estimating Expressivitymentioning

confidence: 99%

Virtually Cloning Real Human with Motion Style

Rajagopal

Horain

Pélachaud

2012

Advances in Intelligent Systems and Computing

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Abstract. Our goal is to capture style from real human motion so it can be rendered with a virtual agent that represents this human user. We used expressivity parameters to describe motion style. As a first contribution, we propose an approach to estimate a subset of expressivity parameters defined in the literature (namely spatial extent and temporal extent) from captured motion trajectories. Second, we capture the expressivity of real users and then output it to the Greta engine that animates a virtual agent representing the user. We experimentally demonstrate that expressivity can be another clue for identifiable virtual clones of real humans.

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Real-time particle filtering with heuristics for 3D motion capture by monocular vision

Cited by 7 publications

References 19 publications

Animating a Conversational Agent with User Expressivity

Animating a Conversational Agent with User Expressivity

Single camera pose estimation using Bayesian filtering and Kinect motion priors

Virtually Cloning Real Human with Motion Style

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