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230
StyleBased Inverse Kinematics
, 2004
"... This paper presents an inverse kinematics system based on a learned model of human poses. Given a set of constraints, our system can produce the most likely pose satisfying those constraints, in realtime. Training the model on different input data leads to different styles of IK. The model is repres ..."
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Cited by 211 (8 self)
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This paper presents an inverse kinematics system based on a learned model of human poses. Given a set of constraints, our system can produce the most likely pose satisfying those constraints, in realtime. Training the model on different input data leads to different styles of IK. The model is represented as a probability distribution over the space of all possible poses. This means that our IK system can generate any pose, but prefers poses that are most similar to the space of poses in the training data. We represent the probability with a novel model called a Scaled Gaussian Process Latent Variable Model. The parameters of the model are all learned automatically; no manual tuning is required for the learning component of the system. We additionally describe a novel procedure for interpolating between styles. Our stylebased
Gaussian process dynamical models for human motion
 IEEE TRANS. PATTERN ANAL. MACHINE INTELL
, 2008
"... We introduce Gaussian process dynamical models (GPDMs) for nonlinear time series analysis, with applications to learning models of human pose and motion from highdimensional motion capture data. A GPDM is a latent variable model. It comprises a lowdimensional latent space with associated dynamics, ..."
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Cited by 158 (5 self)
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We introduce Gaussian process dynamical models (GPDMs) for nonlinear time series analysis, with applications to learning models of human pose and motion from highdimensional motion capture data. A GPDM is a latent variable model. It comprises a lowdimensional latent space with associated dynamics, as well as a map from the latent space to an observation space. We marginalize out the model parameters in closed form by using Gaussian process priors for both the dynamical and the observation mappings. This results in a nonparametric model for dynamical systems that accounts for uncertainty in the model. We demonstrate the approach and compare four learning algorithms on human motion capture data, in which each pose is 50dimensional. Despite the use of small data sets, the GPDM learns an effective representation of the nonlinear dynamics in these spaces.
Performance Animation from Lowdimensional Control Signals
 ACM Transactions on Graphics
, 2005
"... This paper introduces an approach to performance animation that employs video cameras and a small set of retroreflective markers to create a lowcost, easytouse system that might someday be practical for home use. The lowdimensional control signals from the user's performance are supplement ..."
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Cited by 129 (18 self)
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This paper introduces an approach to performance animation that employs video cameras and a small set of retroreflective markers to create a lowcost, easytouse system that might someday be practical for home use. The lowdimensional control signals from the user's performance are supplemented by a database of prerecorded human motion. At run time, the system automatically learns a series of local models from a set of motion capture examples that are a close match to the marker locations captured by the cameras. These local models are then used to reconstruct the motion of the user as a fullbody animation. We demonstrate the power of this approach with realtime control of six different behaviors using two video cameras and a small set of retroreflective markers. We compare the resulting animation to animation from commercial motion capture equipment with a full set of markers.
Gaussian process dynamical models
 in Advances in Neural Information Processing Systems (NIPS
"... This paper introduces Gaussian Process Dynamical Models (GPDM) for nonlinear time series analysis. A GPDM comprises a lowdimensional latent space with associated dynamics, and a map from the latent space to an observation space. We marginalize out the model parameters in closedform, using Gaussian ..."
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Cited by 116 (7 self)
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This paper introduces Gaussian Process Dynamical Models (GPDM) for nonlinear time series analysis. A GPDM comprises a lowdimensional latent space with associated dynamics, and a map from the latent space to an observation space. We marginalize out the model parameters in closedform, using Gaussian Process (GP) priors for both the dynamics and the observation mappings. This results in a nonparametric model for dynamical systems that accounts for uncertainty in the model. We demonstrate the approach on human motion capture data in which each pose is 62dimensional. Despite the use of small data sets, the GPDM learns an effective representation of the nonlinear dynamics in these spaces. Webpage:
Gaussian Processes for Signal StrengthBased Location Estimation
 In Proc. of Robotics Science and Systems
, 2006
"... Abstract — Estimating the location of a mobile device or a robot from wireless signal strength has become an area of highly active research. The key problem in this context stems from the complexity of how signals propagate through space, especially in the presence of obstacles such as buildings, wa ..."
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Cited by 92 (8 self)
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Abstract — Estimating the location of a mobile device or a robot from wireless signal strength has become an area of highly active research. The key problem in this context stems from the complexity of how signals propagate through space, especially in the presence of obstacles such as buildings, walls or people. In this paper we show how Gaussian processes can be used to generate a likelihood model for signal strength measurements. We also show how parameters of the model, such as signal noise and spatial correlation between measurements, can be learned from data via hyperparameter estimation. Experiments using WiFi indoor data and GSM cellphone connectivity demonstrate the superior performance of our approach. I.
Factored Conditional Restricted Boltzmann Machines for Modeling Motion Style
"... The Conditional Restricted Boltzmann Machine (CRBM) is a recently proposed model for time series that has a rich, distributed hidden state and permits simple, exact inference. We present a new model, based on the CRBM that preserves its most important computational properties and includes multiplica ..."
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Cited by 59 (10 self)
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The Conditional Restricted Boltzmann Machine (CRBM) is a recently proposed model for time series that has a rich, distributed hidden state and permits simple, exact inference. We present a new model, based on the CRBM that preserves its most important computational properties and includes multiplicative threeway interactions that allow the effective interaction weight between two units to be modulated by the dynamic state of a third unit. We factorize the threeway weight tensor implied by the multiplicative model, reducing the number of parameters from O(N 3) to O(N 2). The result is an efficient, compact model whose effectiveness we demonstrate by modeling human motion. Like the CRBM, our model can capture diverse styles of motion with a single set of parameters, and the threeway interactions greatly improve the model’s ability to blend motion styles or to transition smoothly between them. 1.
Gaussian Process Latent Variable Models for Human Pose Estimation
"... We describe a generative approach to recover 3D human pose from image silhouettes. Our method is based on learning a shared low dimensional latent representation capable of generating both human pose and image observations through the GPLVM [1]. We learn a dynamical model over the latent space whic ..."
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Cited by 52 (9 self)
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We describe a generative approach to recover 3D human pose from image silhouettes. Our method is based on learning a shared low dimensional latent representation capable of generating both human pose and image observations through the GPLVM [1]. We learn a dynamical model over the latent space which allows us to disambiguate between ambiguous silhouettes by temporal consistency. The model has only two free parameters and requires no manual initialization. 1.
Dynamic imitation in a humanoid robot through nonparametric probabilistic inference
 In Proceedings of Robotics: Science and Systems (RSS’06
, 2006
"... Abstract — We tackle the problem of learning imitative wholebody motions in a humanoid robot using probabilistic inference in Bayesian networks. Our inferencebased approach affords a straightforward method to exploit rich yet uncertain prior information obtained from human motion capture data. Dyna ..."
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Cited by 41 (5 self)
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Abstract — We tackle the problem of learning imitative wholebody motions in a humanoid robot using probabilistic inference in Bayesian networks. Our inferencebased approach affords a straightforward method to exploit rich yet uncertain prior information obtained from human motion capture data. Dynamic imitation implies that the robot must interact with its environment and account for forces such as gravity and inertia during imitation. Rather than explicitly modeling these forces and the body of the humanoid as in traditional approaches, we show that stable imitative motion can be achieved by learning a sensorbased representation of dynamic balance. Bayesian networks provide a sound theoretical framework for combining prior kinematic information (from observing a human demonstrator) with prior dynamic information (based on previous experience) to model and subsequently infer motions which, with high probability, will be dynamically stable. By posing the problem as one of inference in a Bayesian network, we show that methods developed for approximate inference can be leveraged to efficiently perform inference of actions. Additionally, by using nonparametric inference and a nonparametric (Gaussian process) forward model, our approach does not make any strong assumptions about the physical environment or the mass and inertial properties of the humanoid robot. We propose an iterative, probabilistically constrained algorithm for exploring the space of motor commands and show that the algorithm can quickly discover dynamically stable actions for wholebody imitation of human motion. Experimental results based on simulation and subsequent execution by a HOAP2 humanoid robot demonstrate that our algorithm is able to imitate a human performing actions such as squatting and a onelegged balance. I.
Articulated pose estimation in a learned smooth space of feasible solutions
 IN: WORSHOP ON LEARNING IN COMPUTER VISION AND PATTERN RECOGNITION
, 2005
"... A learning based framework is proposed for estimating human body pose from a single image. Given a differentiable function that maps from pose space to image feature space, the goal is to invert the process: estimate the pose given only image features. The inversion is an illposed problem as the in ..."
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Cited by 40 (3 self)
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A learning based framework is proposed for estimating human body pose from a single image. Given a differentiable function that maps from pose space to image feature space, the goal is to invert the process: estimate the pose given only image features. The inversion is an illposed problem as the inverse mapping is a one to many process, hence multiple solutions exist. It is desirable to restrict the solution space to a smaller subset of feasible solutions. The space of feasible solutions may not admit a closed form description. The proposed framework seeks to learn an approximation over such a space. Using Gaussian Process Latent Variable Modelling. The scaled conjugate gradient method is used to find the best matching pose in the learned space. The formulation allows easy incorporation of various constraints for more accurate pose estimation. The performance of the proposed approach is evaluated in the task of upperbody pose estimation from silhouettes and compared with the Specialized Mapping Architecture. The proposed approach performs better than the latter approach in terms of estimation accuracy with synthetic data and qualitatively better results with real video of humans performing gestures.